{"as_of":"2026-08-10T01:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6fa43bffd2226b4aa4735a87fc0e6fe680d61d4184b8c55ff559a05a731ea1b4","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T12:31:22.770408Z","state":"measured"},{"denominator":84,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":84,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:30:15.521820Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T19:30:23.780913Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"cited_work":{"arxiv_id":"2502.02358","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.02358","snapshot_observed_at":"2026-08-06T19:30:23.780913Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","venue":"cs.CV","work_id":"4bee3dc6-b075-4224-9573-b90bcde30f3f","year":2025},"citing_paper":{"arxiv_id":"2507.05419","last_updated":"2025-07-07T19:04:56Z","snapshot_observed_at":"2026-08-06T19:32:58.763798Z","submitted_at":"2025-07-07T19:04:56Z","title":"Motion Generation: A Survey of Generative Approaches and Benchmarks","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:15.521820Z"},"links":{"cited_paper":"/paper/2502.02358","citing_paper":"/paper/2507.05419"},"observation_digest":"sha256:9e58cf2027f124c682f692a41598cb9050e86f7e2fdf522af165a89839639267","observation_id":"1a04f5e0-5aa5-4f04-987a-8da58bb073d2","resolution":{"observed_at":"2026-08-06T19:30:23.786110Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.02358/citation-record","integrity":"/paper/2502.02358/integrity","json":"/paper/2502.02358/citation-record.json","paper":"/paper/2502.02358"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.529196Z","title":"Unpaired motion style transfer from video to animation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.529196Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:91d675d54ec028e0e24d711e5354d92460be0e1e2fe3a69410176f90b8d33bcf","observation_id":"2e61dba7-d822-44b2-b5bc-ecf144f81814","resolution":{"observed_at":"2026-08-09T12:31:22.529196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-09T12:31:22.532875Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.532875Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:55f8962da4a148a12697a09af2d3cba504200a24ee397f504f0976fba41c692a","observation_id":"ea14b70b-8bed-485b-8967-77b1dafe01de","resolution":{"observed_at":"2026-08-09T12:31:22.532875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.536315Z","title":"Listen, denoise, action! audio-driven motion synthesis with diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.536315Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:c11e9f19f8498be44c4f6c27cab04384eb9ec714995d1413c81d0a17ef456356","observation_id":"017292d3-2b74-4a8d-ab17-2ff0e6816f48","resolution":{"observed_at":"2026-08-09T12:31:22.536315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.539764Z","title":"Teach: Temporal action composition for 3d hu- mans","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.539764Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:8433e079fd7e9c8ae817e9831f4f2a4d44c84a37bbd8695fae01c1a7448fb03e","observation_id":"878bf259-0b54-4997-bd05-e58270653136","resolution":{"observed_at":"2026-08-09T12:31:22.539764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.412342Z","title":"Sinc: Spatial composition of 3d human motions for simultaneous action generation","venue":null,"work_id":"4fb504b8-bfb4-4c02-bf8c-7d9eb9c90bf2","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.542959Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:88474614e0c169688bf700f95694d6faebcb621d09abf9524eb08c8dd83e439d","observation_id":"9a7abec4-3f35-4eba-b5ac-03ddae2b8d9e","resolution":{"observed_at":"2026-08-09T12:31:23.415346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.404805Z","title":"Motionfix: Text-driven 3d human motion editing","venue":null,"work_id":"5a401747-6e3f-40f1-be9d-3f5a547dad03","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.546137Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:bc471506fa5f533d7be2da18cbdbdcfd8d74dac04701cb36a1c1e18a9c8ced20","observation_id":"10a96e4c-3614-4e3a-9f00-e71295d91079","resolution":{"observed_at":"2026-08-09T12:31:23.407529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.397556Z","title":"Curriculum learning","venue":null,"work_id":"14552cc3-9cf5-4648-83fe-b313ff97ddf0","year":2009},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.549169Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a2bcbc8e1fef832af91fea69db3e283d4b866ed600b0654e8c3274f4b70d3a16","observation_id":"806ec86d-f81b-4ce2-ba52-3d18eb38492e","resolution":{"observed_at":"2026-08-09T12:31:23.400236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18977","last_updated":"2025-01-22T15:32:45Z","snapshot_observed_at":"2026-08-04T07:15:41.510042Z","submitted_at":"2024-10-24T17:59:45Z","title":"Pay Attention and Move Better: Harnessing Attention for Interactive Motion Generation and Training-free Editing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18977","snapshot_observed_at":"2026-08-09T12:31:22.552477Z","title":"Motionclr: Motion generation and training-free edit- ing via understanding attention mechanisms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.552477Z"},"links":{"cited_paper":"/paper/2410.18977","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:745efe0a391754f688ca67b78aa1681e080995bca0c1a9cc70b872704b34c25f","observation_id":"3060d94e-80db-4a9c-ac66-cb720f642fc0","resolution":{"observed_at":"2026-08-09T12:31:22.552477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.390101Z","title":"Executing your commands via motion diffusion in latent space","venue":null,"work_id":"eae13c3b-eddb-429a-99c3-30885e91bec5","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.555619Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:83d649fcfdfda678c307636d6c9726c2d8dd892f8d9d5d2fe1f4a5066a16e4ae","observation_id":"4a6e5ab5-0cb7-4ae3-9905-1dc020841e83","resolution":{"observed_at":"2026-08-09T12:31:23.392852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14687","last_updated":"2024-05-20T04:23:45Z","snapshot_observed_at":"2026-08-03T03:59:22.374270Z","submitted_at":"2022-09-29T11:12:27Z","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14687","snapshot_observed_at":"2026-08-09T12:31:22.558725Z","title":"Diffusion posterior sam- pling for general noisy inverse problems","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.558725Z"},"links":{"cited_paper":"/paper/2209.14687","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:9d15794ce7d3f9699f28ab479c03e659e7129df19d0852cfd25d15c74f4a913e","observation_id":"48d6bb3f-54a5-4d09-a8d4-776b122370ff","resolution":{"observed_at":"2026-08-09T12:31:22.558725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.382574Z","title":"Flexible motion in-betweening with diffusion models","venue":null,"work_id":"5194f2b9-62e2-4b8e-82aa-4a83a9cc4843","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.561826Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:16aa4309281df28722d632657abfddea071a4e4f0038c8bddf928e61e0d5de1c","observation_id":"466d123f-1f04-4ece-bf67-4a9ae5563f06","resolution":{"observed_at":"2026-08-09T12:31:23.385353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.374988Z","title":"Motionlcm: Real-time controllable motion generation via latent consistency model","venue":null,"work_id":"8b87f6d1-e3a0-4bb8-95b0-e0b59231b053","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.564901Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:af9aafb74cf5111d75e82f8f4941702162f98f3f298f4f6019f294038643da37","observation_id":"79118ec6-cca6-4d24-8f11-ed9b0de16ee4","resolution":{"observed_at":"2026-08-09T12:31:23.377889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.567987Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.567987Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:694cd82d2eed7d7de7a2463d6956062fe928e88276286289610318375d8f162b","observation_id":"bd9f39c4-9969-4472-acd4-6075c522cc74","resolution":{"observed_at":"2026-08-09T12:31:22.567987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-09T12:31:22.570951Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.570951Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:54b44bfa1b3313d10ab8dc488e2f2f9d7c07be2b316d02790af3ee74334c9644","observation_id":"abcf5d4c-162c-4ff0-8615-b8047408be94","resolution":{"observed_at":"2026-08-09T12:31:22.570951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.363334Z","title":"Scaling recti- fied flow transformers for high-resolution image synthesis","venue":null,"work_id":"20db91d4-38fb-4fc6-9362-18146a297e3d","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.574369Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:93ee21c2146c753ee9affca941fda000304005632db1f5cf14b3c21b84987440","observation_id":"bda902db-3c45-4433-b2c9-a3a6f403d5f5","resolution":{"observed_at":"2026-08-09T12:31:23.366260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.355539Z","title":"Everything2motion: Synchronizing diverse inputs via a unified framework for human motion synthesis","venue":null,"work_id":"6bf62f8b-6ed5-447c-88c2-b8774df010c3","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.577158Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:9eee48ba3c94e9db4d368cdf178bd33570fdd86cf6128c5329fb289de43d6019","observation_id":"afe5f726-0933-4336-9b53-4a7ef954900d","resolution":{"observed_at":"2026-08-09T12:31:23.358744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00587","last_updated":"2024-12-20T11:22:01Z","snapshot_observed_at":"2026-08-05T15:59:56.280627Z","submitted_at":"2024-09-01T02:43:33Z","title":"FLUX that Plays Music","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.00587","snapshot_observed_at":"2026-08-09T12:31:22.580132Z","title":"Flux that plays music","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.580132Z"},"links":{"cited_paper":"/paper/2409.00587","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:2b1d17f0a9071f0bfd382019e930093866a14ff1792c39b7f6c1f11f9635db72","observation_id":"1f3e64c8-2d85-48db-a09a-36d7b3d09878","resolution":{"observed_at":"2026-08-09T12:31:22.580132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.348049Z","title":"Chronolog- ically accurate retrieval for temporal grounding of motion- language models","venue":null,"work_id":"58908f4b-d447-4424-b6aa-333b507c544b","year":2025},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.583474Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:2add8ded3d1ed83d9efa551fb859667c5b87f92c00780c9b5c862ec37c99bf9b","observation_id":"5a701b8e-c300-46e7-ad94-419eac031c02","resolution":{"observed_at":"2026-08-09T12:31:23.350811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.340375Z","title":"Iterative motion editing with natural language","venue":null,"work_id":"9622202b-fb94-4e26-96e8-4e50b123ddd3","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.586145Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:1b6f38be5be2f1a970d19448e66577e84e24ad756cc00d5e68c870c1078650e6","observation_id":"3fd26633-4bef-4611-a463-4627dc436524","resolution":{"observed_at":"2026-08-09T12:31:23.343296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.589163Z","title":"Ac- tion2motion: Conditioned generation of 3d human motions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.589163Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:741838e97cee8b500b9cb8cbe7bc1678c41c1748e65be606075b9e896b23cb98","observation_id":"8732b4d9-8cac-4bf3-b963-1ae319219bc8","resolution":{"observed_at":"2026-08-09T12:31:22.589163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.328994Z","title":"Generating diverse and natural 3d human motions from text","venue":null,"work_id":"83a26ca7-c3c0-4e85-b3db-4fa593d66fcc","year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.592153Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:1826b12acd0fdcdcf183cad25d4e954afcb8a77a91779ef53f88751397150370","observation_id":"459d889e-5b53-4387-802e-a6e7b1bb8d27","resolution":{"observed_at":"2026-08-09T12:31:23.331701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.321427Z","title":"Tm2t: Stochastic and tokenized modeling for the reciprocal genera- tion of 3d human motions and texts","venue":null,"work_id":"66b24585-0f68-438b-a2e5-13481f2f2325","year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.595379Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:266b794ffc26483ab34062490c5c3c82e2bf48a8265a4c1e1780facba7825b78","observation_id":"3c0f4eb5-7da8-4ba8-8f43-23e9ddec8d3e","resolution":{"observed_at":"2026-08-09T12:31:23.324338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.313949Z","title":"Momask: Generative masked model- ing of 3d human motions","venue":null,"work_id":"e7444ccf-462d-4629-90a9-13042c514011","year":1900},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.598465Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:b9d3a66050dad27f96b799a0e30fa8a5b750bad4e0e7a302045c63f981ee9775","observation_id":"e92458f4-812e-492a-9481-4b61856559e6","resolution":{"observed_at":"2026-08-09T12:31:23.316760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.13505","last_updated":"2024-02-24T03:18:17Z","snapshot_observed_at":"2026-07-06T17:19:56.099931Z","submitted_at":"2024-01-24T14:53:13Z","title":"Generative Human Motion Stylization in Latent Space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.13505","snapshot_observed_at":"2026-08-09T12:31:22.601810Z","title":"Generative human motion stylization in latent space","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.601810Z"},"links":{"cited_paper":"/paper/2401.13505","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:464a6603269cd03843fde5a843bbbe14d9054b55cd995f711ee29904fbcd9f07","observation_id":"cd4019f9-7654-4a6c-a330-d69899f6cf2c","resolution":{"observed_at":"2026-08-09T12:31:22.601810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.01182","last_updated":"2025-05-05T05:14:20Z","snapshot_observed_at":"2026-08-07T15:57:05.668588Z","submitted_at":"2025-05-02T10:50:04Z","title":"TSTMotion: Training-free Scene-aware Text-to-motion Generation","version":2},"cited_work":{"arxiv_id":"2505.01182","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.01182","snapshot_observed_at":"2026-08-09T12:31:22.996104Z","title":"TSTMotion: Training-free Scene-aware Text-to-motion Generation","venue":"cs.CV","work_id":"c83e427f-6e92-4cda-a05e-b56a05521cc0","year":2025},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.605420Z"},"links":{"cited_paper":"/paper/2505.01182","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:fc6845ca716012f3742218869cce3159553e4d731fe864c38608dca738f60063","observation_id":"c5fd513e-8e1d-4682-82d2-a1e2526fe9a9","resolution":{"observed_at":"2026-08-09T12:31:22.999369Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.306556Z","title":"Robust motion in-betweening","venue":null,"work_id":"fdc9f5fa-e023-46b4-8b4f-cb5700a7ff4a","year":2020},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.608772Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:ae9209d556f9e23f3b9466145c318814821b30da76a82d7f1ed50562c2c7efef","observation_id":"61f51d53-6295-4990-a23e-4c66b0e03caf","resolution":{"observed_at":"2026-08-09T12:31:23.309244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-07-26T01:42:07Z","title":"Classifier-Free Diffusion Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12598","snapshot_observed_at":"2026-08-09T12:31:22.611621Z","title":"Classifier-free diffusion guidance","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.611621Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:96a6d2dd57dfa6e298c910f04fce8b6c813a5a5a11a8dfc2d7ab207586ef25c2","observation_id":"5095791c-66c3-4609-86ca-673eacf55247","resolution":{"observed_at":"2026-08-09T12:31:22.611621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.615017Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.615017Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:ad57c159d651220e2337f3b4a8b0b78ae1b46308ad2cfb42207a593d9c89e88a","observation_id":"904bcebe-da63-4a73-9c70-c673ac5690a2","resolution":{"observed_at":"2026-08-09T12:31:22.615017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.294743Z","title":"Mo- tion puzzle: Arbitrary motion style transfer by body part","venue":null,"work_id":"32a0c292-a887-4168-adc0-a76f0e5cef3d","year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.617845Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:076fcf79bf79cd9cbb0955df31a75711e0130de1e615fb909964a962f6de429e","observation_id":"0342eb24-9f21-461d-8d0f-1a5483e26548","resolution":{"observed_at":"2026-08-09T12:31:23.297526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.287127Z","title":"Motiongpt: Human motion as a foreign lan- guage","venue":null,"work_id":"5e6039df-f8b2-4f80-8b8f-e16d79e39e00","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.620609Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:8a4d14f14a6cde1f489c8bfb680f1dc5ad6ea6510e1f91a7e47ef35e795e480b","observation_id":"0808d5f9-9e59-4789-8009-d3897b65f060","resolution":{"observed_at":"2026-08-09T12:31:23.289951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.279522Z","title":"Motionchain: Conversational motion controllers via multimodal prompts","venue":null,"work_id":"eeb379b7-97da-4ed2-9f01-36c939f68ea5","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.623425Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:3261e4abd5662748333d69cdb463e80e95e07968174ba3238f90e67e2f8a4ebd","observation_id":"231e3ee7-1af8-4b04-8b27-de81822f005e","resolution":{"observed_at":"2026-08-09T12:31:23.282270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.271856Z","title":"Guided motion diffusion for controllable human motion synthesis","venue":null,"work_id":"a74f3339-4059-49f8-94e4-b051a299bab1","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.626436Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:876e241ae765fbf6aca59f09bd6403f9c737261e7b9fb863b2ed512403c42a94","observation_id":"7ed86a9f-29b3-4291-8045-ee56826c6918","resolution":{"observed_at":"2026-08-09T12:31:23.274745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.263985Z","title":"Flame: Free- form language-based motion synthesis & editing","venue":null,"work_id":"6636620f-ac39-4479-90ff-29c3715d4a98","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.629315Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:fda39757e2060c6d089a0d1748fc18aed902e5c5bcf6b24c2ac8610aa768c270","observation_id":"e0f21f5c-da21-4d98-b168-85260543da1f","resolution":{"observed_at":"2026-08-09T12:31:23.267083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15904","last_updated":"2024-09-30T10:39:38Z","snapshot_observed_at":"2026-08-05T13:28:25.630407Z","submitted_at":"2024-09-24T09:20:06Z","title":"Unimotion: Unifying 3D Human Motion Synthesis and Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15904","snapshot_observed_at":"2026-08-09T12:31:22.631985Z","title":"Unimotion: Unify- ing 3d human motion synthesis and understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.631985Z"},"links":{"cited_paper":"/paper/2409.15904","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:99300ddbaf01d2d357a920ccd77967428db065f07e8cb5d73317a33e3182af99","observation_id":"1629a825-5459-4744-8b4e-0f9931b6dad3","resolution":{"observed_at":"2026-08-09T12:31:22.631985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.256360Z","title":"Motion-x: A large-scale 3d expressive whole-body human motion dataset","venue":null,"work_id":"2d524e9a-dc5d-4e11-a582-14cdbef3765b","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.634948Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:956cf1050306620dfb4dd1d891e3df832e51f224291533c34abb36ddf1de8ff1","observation_id":"b879c1fa-9fa8-4268-a1d6-25d835874955","resolution":{"observed_at":"2026-08-09T12:31:23.259279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17335","last_updated":"2025-05-26T15:27:18Z","snapshot_observed_at":"2026-07-06T19:57:14.289258Z","submitted_at":"2024-11-26T11:28:01Z","title":"VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.17335","snapshot_observed_at":"2026-08-09T12:31:22.637841Z","title":"Motionllama: A unified framework for motion synthesis and comprehension","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.637841Z"},"links":{"cited_paper":"/paper/2411.17335","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:558faa456fdd67b97880a9373156eaa97434ccc4026be2d204de92a0f0bf5444","observation_id":"2f6ea7e6-0658-4b9e-aa94-e65e42ec3925","resolution":{"observed_at":"2026-08-09T12:31:22.637841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-02T18:24:58.914589Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-09T12:31:22.640727Z","title":"Flow matching for generative mod- eling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.640727Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:286823f96bdf61e91edd25f4252572c5f3ed25b05907b9589566f2c4922aa057","observation_id":"225e668f-2813-468c-991a-3cd6a9c18a5a","resolution":{"observed_at":"2026-08-09T12:31:22.640727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-09T12:31:22.643702Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.643702Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:f43f18e3517c695d3a5f07162e830652d264afdcefa814008aae7ee714e6d926","observation_id":"8a9267c2-cf27-4990-9cf2-9c2fe6aa45db","resolution":{"observed_at":"2026-08-09T12:31:22.643702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.646411Z","title":"Smpl: A skinned multi- person linear model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.646411Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:988c03bf774a5ef6907b5854944a1ef95dbbdc2d8856735111fb90fe36d5bf02","observation_id":"8a2ab0e7-ac8d-428c-873c-685aaf85bb0b","resolution":{"observed_at":"2026-08-09T12:31:22.646411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12978","last_updated":"2023-10-19T17:59:46Z","snapshot_observed_at":"2026-08-03T23:57:00.735076Z","submitted_at":"2023-10-19T17:59:46Z","title":"HumanTOMATO: Text-aligned Whole-body Motion Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.12978","snapshot_observed_at":"2026-08-09T12:31:22.649012Z","title":"Humantomato: Text-aligned whole-body motion generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.649012Z"},"links":{"cited_paper":"/paper/2310.12978","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:fc4d0e7f684dd2a9688219efae24e856d5ee0bc067cdb10719ed6ecb60045752","observation_id":"1314705d-38c8-4388-9b58-cbee3f05808d","resolution":{"observed_at":"2026-08-09T12:31:22.649012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16273","last_updated":"2024-11-02T04:39:28Z","snapshot_observed_at":"2026-07-06T18:19:52.121906Z","submitted_at":"2024-05-25T15:21:59Z","title":"M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16273","snapshot_observed_at":"2026-08-09T12:31:22.651850Z","title":"M3gpt: An advanced multimodal, multitask framework for motion comprehension and generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.651850Z"},"links":{"cited_paper":"/paper/2405.16273","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:b2ca53f703ad74c6379a2e6039a0a97ebfe5f4ec3c8d5d1a6deb3ed041c24a4a","observation_id":"d0a47b5d-6d29-41b3-afc3-d1bbc95c7208","resolution":{"observed_at":"2026-08-09T12:31:22.651850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08740","last_updated":"2024-09-23T15:59:41Z","snapshot_observed_at":"2026-08-07T23:50:09.060564Z","submitted_at":"2024-01-16T18:55:25Z","title":"SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08740","snapshot_observed_at":"2026-08-09T12:31:22.654723Z","title":"Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.654723Z"},"links":{"cited_paper":"/paper/2401.08740","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:c8766075d4887b4650856c48bf41a7c814839623e5de848476fa8e7dc5b43455","observation_id":"179c48d2-9fd3-4fcb-a9ab-d0ea682be35b","resolution":{"observed_at":"2026-08-09T12:31:22.654723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.657972Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.657972Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:0b30faed39be8d6561033301bba975539971620792cbb969f8a1c1ac10b83863","observation_id":"1bf434f6-f20f-4236-84c9-6d9a395ef71e","resolution":{"observed_at":"2026-08-09T12:31:22.657972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.660687Z","title":"Action- conditioned 3d human motion synthesis with transformer vae","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.660687Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:036096b083f8bda91d2562ba3df809bc822b1911dd7493b02bf61a7afac45e7c","observation_id":"6c2fdcb4-7c44-42b9-b8ab-d2e97131ba86","resolution":{"observed_at":"2026-08-09T12:31:22.660687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.237457Z","title":"Mmm: Generative masked motion model","venue":null,"work_id":"7f0f0197-9487-4a55-a2c6-0b1cfe5acfde","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.663324Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:408accb3d3ed57f7127e10786590f3aad0d9b0b24f252087d243ab17e3f1b25f","observation_id":"015b7eba-d16b-42e2-9109-6beb170f1019","resolution":{"observed_at":"2026-08-09T12:31:23.240283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.665950Z","title":"The kit motion-language dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.665950Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a2b6c1ff2ffd4b9727196dfa071c91a644a746d609eb2d9fb03391e1da81d7b3","observation_id":"66c59a0b-0f06-4e91-91fa-f3faa25dd9c1","resolution":{"observed_at":"2026-08-09T12:31:22.665950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.225602Z","title":"Sampson, Shikai Li, Simone Parmeggiani, Steve Fine, Tara Fowler, Vladan Petro- vic, and Yuming Du","venue":null,"work_id":"311b7aaf-080c-4bdd-adc8-f71ca90f15fe","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.668660Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:86a0a0c67404a2b020d6d68043237c1023a50bc697d765d56b6916b2e2d3f815","observation_id":"7da3869d-a8b6-4e1c-8c01-c04a43568a50","resolution":{"observed_at":"2026-08-09T12:31:23.228694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.217825Z","title":"Motion in-betweening via two-stage transformers","venue":null,"work_id":"675bcc3f-804c-4cbb-ac9d-06147b5d9b15","year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.671276Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:6b4e993756230ceb7ca3f9e82f01b48917170e13cde646f3603d78c967d959cf","observation_id":"df22a93e-830b-42c9-90b5-4f14ba985baf","resolution":{"observed_at":"2026-08-09T12:31:23.220638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.210239Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"bbd10c5c-bedf-4cc5-b801-2f7f5129c136","year":2021},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.673751Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:92b556024a0eb9dc3e0e9a61bd003ab0bf0db3029e336131f9b1f760a389903a","observation_id":"b13d352d-fb97-464e-9b29-5e03f8d68f19","resolution":{"observed_at":"2026-08-09T12:31:23.213045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11532","last_updated":"2024-07-16T09:30:57Z","snapshot_observed_at":"2026-07-06T18:47:04.659942Z","submitted_at":"2024-07-16T09:30:57Z","title":"Length-Aware Motion Synthesis via Latent Diffusion","version":1},"cited_work":{"arxiv_id":"2407.11532","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.11532","snapshot_observed_at":"2026-08-09T12:31:22.856958Z","title":"Length-Aware Motion Synthesis via Latent Diffusion","venue":"cs.CV","work_id":"0616b6bd-bd93-417e-b123-9a5e8268d912","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.676550Z"},"links":{"cited_paper":"/paper/2407.11532","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:143b2e62f03e9444afbb55c730f73a6c3617280f82a6ed72aa76df1b1e068017","observation_id":"7d1aa27b-94d8-4b7f-a1af-7c6dd42c31da","resolution":{"observed_at":"2026-08-09T12:31:22.861800Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.01418","last_updated":"2023-08-30T04:41:10Z","snapshot_observed_at":"2026-08-04T22:38:03.106382Z","submitted_at":"2023-03-02T17:09:27Z","title":"Human Motion Diffusion as a Generative Prior","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.01418","snapshot_observed_at":"2026-08-09T12:31:22.679500Z","title":"Human motion diffusion as a generative prior","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.679500Z"},"links":{"cited_paper":"/paper/2303.01418","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a676cabb281769b93d0e9f418d9ef7167b5e02159e2891e41b3ea293ac59c426","observation_id":"4810b091-9baa-4b4d-8590-abaa1ef717d2","resolution":{"observed_at":"2026-08-09T12:31:22.679500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.202794Z","title":"Generating physically realistic and directable human motions from multi-modal inputs","venue":null,"work_id":"1a3e421b-089e-4d6c-aa99-bb2a1387b3c5","year":2025},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.682610Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:cc1f14999d836a671e14ab1000c9f4beeabafd39e5f7453979f24ce9b4745b14","observation_id":"7d2c999c-061c-4910-83b4-4bc47c608ecf","resolution":{"observed_at":"2026-08-09T12:31:23.205599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-09T12:31:22.685503Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.685503Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:7812c5b87ab0d304564094686fce4723655fbdc688d8660769bc96a65d23fc69","observation_id":"675086a5-e922-4efd-a6b1-67f7e6337269","resolution":{"observed_at":"2026-08-09T12:31:22.685503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.195181Z","title":"Loss-guided diffusion models for plug-and-play controllable generation","venue":null,"work_id":"396f2fce-854c-4482-89b2-d4d3c9f09ff8","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.688360Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:95a21bd841590762525a305ad4b70e64d879927431defa214c3155deb893dacf","observation_id":"59d86401-c012-4f83-a2fb-b664920f28ba","resolution":{"observed_at":"2026-08-09T12:31:23.198127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.187821Z","title":"Arbitrary motion style transfer with multi-condition motion latent dif- fusion model","venue":null,"work_id":"9d2870e7-ff55-46bb-a524-ea92fc750937","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.691229Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:4f1a1414353f6f9b7e16dcf09599d64f6b7ce062035878a92d9d1b05604a3530","observation_id":"1d0a71a7-ef8f-47be-9a68-b9f6f6fa0db8","resolution":{"observed_at":"2026-08-09T12:31:23.190635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.694067Z","title":"Roformer: Enhanced transformer with rotary position embedding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.694067Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:253a17d793ad33c00a2ce3f8acc0d752087b1fc58278510443e4423b66aaabc9","observation_id":"9e33d960-f6ae-41b6-ad7a-3565b765844a","resolution":{"observed_at":"2026-08-09T12:31:22.694067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.176183Z","title":"Lgtm: Local-to-global text- driven human motion diffusion model","venue":null,"work_id":"b97cdfa2-c39e-4223-9b80-67bcb0b01ba5","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.696877Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:48e36d4460b584705be9304cdeb70aa13e1c2df4c02f9861bebd44827b603f2a","observation_id":"23e96630-cb2d-42fc-ae47-2b0c797d2f80","resolution":{"observed_at":"2026-08-09T12:31:23.179113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.168803Z","title":"Motionclip: Exposing human motion generation to clip space","venue":null,"work_id":"fc0a69df-f7ad-4fe7-93d3-698cadd2a161","year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.699642Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:b0c65927ea6f5fb90da7c478be009297456e8ff1298070e0747417c1211a0042","observation_id":"321901ea-7168-4cc5-967b-c23ecf5c0d8d","resolution":{"observed_at":"2026-08-09T12:31:23.171649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.161404Z","title":"Human motion diffu- sion model","venue":null,"work_id":"23de6695-1d5c-433d-be17-174ae1d8e028","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.702448Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:0f76cf3326ee9921a01ce514838bc774ab58213ba8c55bf65875beeb2020ed73","observation_id":"d75dadd6-7904-4543-afcc-ccd0a5ea6fc6","resolution":{"observed_at":"2026-08-09T12:31:23.164117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:22.705328Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.705328Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:52ad5f828161aa60e427a537d5ed004678695db8b4da40e95fb85af62725f185","observation_id":"787375dc-db2a-405d-bdc5-b4921b0184b9","resolution":{"observed_at":"2026-08-09T12:31:22.705328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00320","last_updated":"2025-01-04T18:12:07Z","snapshot_observed_at":"2026-07-06T18:23:39.931536Z","submitted_at":"2024-06-01T06:40:22Z","title":"Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00320","snapshot_observed_at":"2026-08-09T12:31:22.708098Z","title":"Frieren: Efficient video-to-audio generation with rectified flow matching","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.708098Z"},"links":{"cited_paper":"/paper/2406.00320","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a8f7a27c06d9bdd2351277c55daf04b27567f5a378ab242e52981640dbc6d983","observation_id":"370dbea5-880b-47f6-9edb-73aafb1b3fb1","resolution":{"observed_at":"2026-08-09T12:31:22.708098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21747","last_updated":"2026-06-08T02:14:45Z","snapshot_observed_at":"2026-08-09T10:12:49.943251Z","submitted_at":"2024-10-29T05:25:34Z","title":"MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21747","snapshot_observed_at":"2026-08-09T12:31:22.711260Z","title":"Motiongpt-2: A general-purpose motion- language model for motion generation and understanding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.711260Z"},"links":{"cited_paper":"/paper/2410.21747","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a5c311ebadaa6d84740deeccf7f5d0477c599c7d637e9b9e494b5316c19ec633","observation_id":"940b92b8-8503-4c28-bf2f-1c653b5a57ef","resolution":{"observed_at":"2026-08-09T12:31:22.711260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17013","last_updated":"2024-10-06T13:46:47Z","snapshot_observed_at":"2026-08-04T14:25:14.829572Z","submitted_at":"2024-05-27T09:57:51Z","title":"Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17013","snapshot_observed_at":"2026-08-09T12:31:22.714200Z","title":"Motionllm: Multimodal motion-language learning with large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.714200Z"},"links":{"cited_paper":"/paper/2405.17013","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:00b93435d3ac66e0b40e59fe2277bb808dd6baf1b73ad5144bff5bf571da72cb","observation_id":"064134e4-c3ed-4d9f-89cd-a1a7bfcf94ff","resolution":{"observed_at":"2026-08-09T12:31:22.714200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08580","last_updated":"2024-04-14T22:23:18Z","snapshot_observed_at":"2026-08-09T16:12:38.222023Z","submitted_at":"2023-10-12T17:59:38Z","title":"OmniControl: Control Any Joint at Any Time for Human Motion Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08580","snapshot_observed_at":"2026-08-09T12:31:22.717302Z","title":"Omnicontrol: Control any joint at any time for human motion generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.717302Z"},"links":{"cited_paper":"/paper/2310.08580","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:0ffee7744ce99bbaaa83fa06eafb8fae91bd43a6e1e098d456ceeb0bf33847fc","observation_id":"eba7d1e5-6520-471c-876c-77b869deafbc","resolution":{"observed_at":"2026-08-09T12:31:22.717302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04534","last_updated":"2024-10-06T16:04:05Z","snapshot_observed_at":"2026-08-06T16:42:34.220032Z","submitted_at":"2024-10-06T16:04:05Z","title":"UniMuMo: Unified Text, Music and Motion Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04534","snapshot_observed_at":"2026-08-09T12:31:22.720297Z","title":"Unimumo: Uni- fied text, music and motion generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.720297Z"},"links":{"cited_paper":"/paper/2410.04534","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:d2d2f45272cd0ca8b159c58d783189861e4bc22293d5593e76319144228e9f9d","observation_id":"0ea01515-9608-4e5c-8e91-94e2ac564f66","resolution":{"observed_at":"2026-08-09T12:31:22.720297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.149896Z","title":"Generating human motion from textual descrip- tions with discrete representations","venue":null,"work_id":"c33c6f98-d957-4b63-bbce-93d60faf02d6","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.723816Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:214538f8ab388f46a5c16f42e698632576563012ec2b6f7b171cec0bf3e1c37e","observation_id":"5d0f8b3f-61d2-418c-9417-5525139c7afc","resolution":{"observed_at":"2026-08-09T12:31:23.152765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.15001","last_updated":"2022-08-31T17:58:54Z","snapshot_observed_at":"2026-08-09T16:31:06.714132Z","submitted_at":"2022-08-31T17:58:54Z","title":"MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.15001","snapshot_observed_at":"2026-08-09T12:31:22.726609Z","title":"Motiondif- fuse: Text-driven human motion generation with diffusion model","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.726609Z"},"links":{"cited_paper":"/paper/2208.15001","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a7ca79d76cfa7193c2e716cab58c0a638ef1b91f202a834f3b4ba19568fa9639","observation_id":"5c661101-30ba-41e0-8dea-6066b3094016","resolution":{"observed_at":"2026-08-09T12:31:22.726609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.142169Z","title":"Finemogen: Fine-grained spatio- temporal motion generation and editing","venue":null,"work_id":"4b1154a5-888c-46d1-b8c9-2b1a9b685420","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.729582Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:9ed12611e0f567955e2e1fc02a062147f0c2469c2a35b165752a148bac48882b","observation_id":"f3957aca-b600-49ea-a405-11c33fc1a7d6","resolution":{"observed_at":"2026-08-09T12:31:23.145365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.134873Z","title":"Large motion model for unified multi-modal motion generation","venue":null,"work_id":"d91fbb39-63fe-4c59-97f9-99573702e2ae","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.732353Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:96fb2fc2b32ec574310ed3ca38c4b821d6affd6606403329e215ad15525dce70","observation_id":"e33b95f4-14f6-439a-8068-524b30ea61d7","resolution":{"observed_at":"2026-08-09T12:31:23.137762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.18128","last_updated":"2024-09-26T17:59:51Z","snapshot_observed_at":"2026-07-06T19:22:58.341345Z","submitted_at":"2024-09-26T17:59:51Z","title":"FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.18128","snapshot_observed_at":"2026-08-09T12:31:22.735505Z","title":"Flowturbo: Towards real-time flow-based image genera- tion with velocity refiner","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.735505Z"},"links":{"cited_paper":"/paper/2409.18128","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a5ef41f92d691c033c31095fcba168b9a8e5915b715f468df1a261564a04f232","observation_id":"f5416554-c360-4ad2-8b92-b1120313aa05","resolution":{"observed_at":"2026-08-09T12:31:22.735505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.127403Z","title":"Smoodi: Stylized motion diffusion model","venue":null,"work_id":"b3eea3db-f10b-413d-bc7d-59b919f0a8d7","year":2025},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.738314Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:92f02e702aba644b990fdf8be4a865630cb6cc5b3c5f429b9044dec03876e019","observation_id":"19821d65-4e99-4f07-b783-95e9d608ce89","resolution":{"observed_at":"2026-08-09T12:31:23.130311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.119688Z","title":"Ude: A unified driv- ing engine for human motion generation","venue":null,"work_id":"0e786866-455f-40e4-827d-3573f0965a00","year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.740823Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:1073d9103815f801c3f58c7dcd7b01839386c8291beac3be8eb83725d07e25e4","observation_id":"699a9df4-d702-48c4-8d01-da2e19e6b908","resolution":{"observed_at":"2026-08-09T12:31:23.122585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16471","last_updated":"2023-11-28T04:13:49Z","snapshot_observed_at":"2026-07-06T16:53:35.776613Z","submitted_at":"2023-11-28T04:13:49Z","title":"A Unified Framework for Multimodal, Multi-Part Human Motion Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.16471","snapshot_observed_at":"2026-08-09T12:31:22.743528Z","title":"A unified frame- work for multimodal, multi-part human motion synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.743528Z"},"links":{"cited_paper":"/paper/2311.16471","citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:c470cdb6f7f1423fb7a02f1723c68f971a5a987b4da2b2b969eb0c7568b88d1d","observation_id":"38a9a5a6-aa0d-4076-acf6-89a2304329e8","resolution":{"observed_at":"2026-08-09T12:31:22.743528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.111607Z","title":"Avatargpt: All- in-one framework for motion understanding planning gener- ation and beyond","venue":null,"work_id":"6c913f51-ee6d-4eb3-9da6-12d5ee694e44","year":2024},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.746508Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:a81da2ec606b5fbda2d91662e518d92ca7f51b68df5888df5e346244e399ea6c","observation_id":"81776035-1d90-4076-b7a0-e3c3cb8e87f7","resolution":{"observed_at":"2026-08-09T12:31:23.114520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.103713Z","title":"Demonstration of the difference between diffusion mod- els and rectified flows","venue":null,"work_id":"b0a4a76a-bd4f-4e39-8662-1219eb3b431c","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.749095Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:9b5b8eb3aa66424644f3613487ae5fb35f3bfdbe74e3a99807796784395ba90b","observation_id":"7257106f-f314-4125-8bd1-3d7ef68b320c","resolution":{"observed_at":"2026-08-09T12:31:23.106750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.095777Z","title":null,"venue":null,"work_id":"edcc510e-1558-4232-928a-f24e951b991e","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.751884Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:accb130c13ffc53744750569d5914568a3b1787808a00cc3b0748616555b86d8","observation_id":"e136804c-5c4f-4876-b3a4-5a05385b6a15","resolution":{"observed_at":"2026-08-09T12:31:23.098625Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.088285Z","title":"The memory usage and the time spent during inference are summarized in the following Table 7","venue":null,"work_id":"40d496d0-8404-4d2f-9a68-0ea44c10a6a7","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.754383Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:2205356993643ed40be951f726047e395a786cd5821078b7639e16b451cd5486","observation_id":"09562547-0894-46d6-95cc-79a2ef9819d9","resolution":{"observed_at":"2026-08-09T12:31:23.091126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.080347Z","title":null,"venue":null,"work_id":"8f5517b3-8e11-457d-b20e-9a6bebef6975","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.757152Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:c4a9017efde00ef935b2f678d1ae2cb5c683dee5c9048dd9ea1ba65225895a2d","observation_id":"4a512897-04c2-4867-99f3-4ffab22e835f","resolution":{"observed_at":"2026-08-09T12:31:23.083241Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.072545Z","title":"w/o task instruction modulation","venue":null,"work_id":"b2a57fa4-7015-4825-b4e8-0a99b2898a40","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.759672Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:c5f14b90a92f0b65e1759c5c2e46a698f4786d28e20c2d38e2defe5bde0d8726","observation_id":"6903e4ef-dcba-4fa3-ae5b-3c51e6db4b31","resolution":{"observed_at":"2026-08-09T12:31:23.075386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.064860Z","title":"Specifically, source motion and target motion are represented as MS ∈ RN ×D and MT ∈ RN ×D, and we first ignore timestep t here","venue":null,"work_id":"b81401f1-8de3-4e28-a57c-abf1d5147919","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.762417Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:3838ebc971bbe72aa0eb60ace54043650c2146ba003d2a4658d78022552a17b4","observation_id":"3ef9591c-574e-4c13-9f5f-9de805583b60","resolution":{"observed_at":"2026-08-09T12:31:23.067733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.057057Z","title":null,"venue":null,"work_id":"2c322fc0-87f6-4fda-88fb-8eda07d38f89","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.765096Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:38507d2b4b52712f93c8daf7d8b38c4120e59d96c86f26f0bc2385ca7ea6ebcc","observation_id":"83237aed-3fee-4bec-9e2d-90c9f650aa96","resolution":{"observed_at":"2026-08-09T12:31:23.059719Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.048945Z","title":"We conduct ablation experiments based on the hyperparameters provided by the baseline and finally obtain the above hyperparameters","venue":null,"work_id":"7be925f9-9695-4fce-8ec0-975a00fcd1aa","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.767768Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:27d7f414b67bf3f7ed107c810946611dfe0f9e91b705763e389382bc25709926","observation_id":"98893896-862c-4ea1-9350-57800604fc3b","resolution":{"observed_at":"2026-08-09T12:31:23.052099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:31:23.041022Z","title":"reconstruct given masked source motion","venue":null,"work_id":"295c9212-b42c-4de6-8732-c6473a9d40c2","year":null},"citing_paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","version":5},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-09T12:31:22.770408Z"},"links":{"citing_paper":"/paper/2502.02358"},"observation_digest":"sha256:d8708086962bed830b4c1e9eb08c053f1562645208b23e4e5e47f00574d1fb46","observation_id":"dfdf2f00-398d-45a9-95d1-5d48b2b9d3fc","resolution":{"observed_at":"2026-08-09T12:31:23.043924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.02358","last_updated":"2025-07-22T13:47:07Z","latest_version":5,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T12:23:45.729050Z","submitted_at":"2025-02-04T14:43:26Z","title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":39,"verified_exact":2,"verified_fuzzy":42},"total_outbound_references":83},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 1 inbound Pith citation observation for arXiv:2502.02358."}