{"as_of":"2026-08-10T16:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8daff8edba5bdd588b56c3c41758a4a60daca55880a90552a90074343a3ad898","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T16:19:06.414648Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.01190/citation-record","integrity":"/paper/2502.01190/integrity","json":"/paper/2502.01190/citation-record.json","paper":"/paper/2502.01190"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T16:19:06.900926Z","title":"Fpn-gan: multi-class small object detection in remote sensing images","venue":null,"work_id":"72ec7f1e-312b-4a87-950a-f3be85a8dac3","year":2021},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.259724Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:1610f69c318f159f57b723576166a5f391ae998f481712ff94f9d9e3bd2b415c","observation_id":"1a880b16-fb11-400b-9eec-6195c20c089b","resolution":{"observed_at":"2026-08-09T16:19:06.905934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.889803Z","title":"Text2action: Generative adversarial synthesis from language to action","venue":null,"work_id":"90862c1c-ff6e-4891-9923-29b90b10dd34","year":2018},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.264578Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:3e15521b93bf69674122f3d27572c4d62df217039af6a749dbf995f4c74dd256","observation_id":"dfbdc22b-8d72-433c-a979-8f42a3a7382a","resolution":{"observed_at":"2026-08-09T16:19:06.893645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.878994Z","title":"Language2 pose: Natural language grounded pose forecasting","venue":null,"work_id":"0d8af8ba-a9d0-4040-965b-2eed6e76d982","year":2019},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.268479Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:db214d360599e8254d0491b8d30d25792855023e71c9f5995879fc61c2f8f3ab","observation_id":"bd9780b2-b4b9-4c9e-b726-3ea4a84b4695","resolution":{"observed_at":"2026-08-09T16:19:06.882892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.868517Z","title":"Vivit: A video vision transformer","venue":null,"work_id":"b095f212-e2c5-4e7d-9b51-5428c7340f2d","year":2021},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.272149Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:5763801f5d3ec9069b9f4b821fc20bdd5c4909079e0fcdcf6310dcd9adf3e9dd","observation_id":"b9359350-acd7-48bf-98b1-72fe28c80ceb","resolution":{"observed_at":"2026-08-09T16:19:06.872319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.857650Z","title":"Teach: Temporal action composition for 3d humans","venue":null,"work_id":"d544a705-3db3-41db-ac81-38ba0a4a2982","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.276020Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:6f6954920dec5fed94774bac5d34bf37e538b2c34b663efc3a33b97b65070cb1","observation_id":"24159684-2798-4d81-9c07-30d7f54c35f5","resolution":{"observed_at":"2026-08-09T16:19:06.861455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.847092Z","title":"Executing your commands via motion diffusion in latent space","venue":null,"work_id":"516da807-e250-4cea-bba2-d79b9d203be3","year":2023},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.279865Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:89403cec62c4e1d190c0b73f209a27badd3452989018fc1d914771a02988fd1f","observation_id":"6c297dbd-36cd-459b-85a9-0b4f80279759","resolution":{"observed_at":"2026-08-09T16:19:06.850934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.834688Z","title":"Generative ad- versarial graph convolutional networks for human action sy nthesis","venue":null,"work_id":"68c25891-5e8d-4267-8d86-c2610309bdde","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.284017Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:c5dce634041c1afd6eedb46e367670ca685d4794c40e2ae06566d60b44215bf0","observation_id":"6e46b5c7-6abe-4f38-8597-26a0067c4439","resolution":{"observed_at":"2026-08-09T16:19:06.840098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.822420Z","title":"Gate-variants of gated recur rent unit (gru) neural networks","venue":null,"work_id":"a2c799a5-1928-4217-b3c5-9fc6918f0945","year":2017},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.287833Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:ec294c09d9c8b9f109b81b3879378856fd935d611a569e17205d9970589769ef","observation_id":"6810f513-ae6c-4419-bd4f-e226dd3f0e09","resolution":{"observed_at":"2026-08-09T16:19:06.827107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.810893Z","title":"Multiscale vision transformers","venue":null,"work_id":"a41ff8b8-6b1e-47cd-a0a1-167e2228a508","year":2021},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.291455Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:bec3669c8ad034bf2d3db4e62a9716b56a8b12fd5be0b5a2619b6a196349f091","observation_id":"29dd0ce1-0845-4275-81c4-8f320b51513c","resolution":{"observed_at":"2026-08-09T16:19:06.814896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.798310Z","title":"Long video generation with time-agnostic vqgan and time-se nsitive transformer","venue":null,"work_id":"22733513-e915-4a65-a9da-09ecbcc65999","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.295001Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:10d5273e3b15082112b4d3d216a510695bc5fc683e5316e3285747690895cb11","observation_id":"7dbc5413-1251-49b4-983e-37286c889ab9","resolution":{"observed_at":"2026-08-09T16:19:06.802300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.786988Z","title":"Synthesis of com- positional animations from textual descriptions","venue":null,"work_id":"47bc61b8-ee69-4a69-8654-1d10f3947289","year":2021},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.298751Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:e2416553dca71d401f83c133d8f17324e26f1f362fb204e7961c324a97a5b230","observation_id":"1422f1b6-3c4f-4f41-a1bb-8df7451dd07b","resolution":{"observed_at":"2026-08-09T16:19:06.791292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.775967Z","title":"Tm2t: Stoc hastic and tokenized modeling for the recipro- cal generation of 3d human motions and texts","venue":null,"work_id":"ff902bfb-070b-4e69-88e5-cad9114bb3ab","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.302463Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:1146d1603e7a76b5d12a0be4f73bdbbb9f1a7355e3a349c14f1391fc61518afa","observation_id":"dfb67ec3-dc04-4590-90d4-e05e75384199","resolution":{"observed_at":"2026-08-09T16:19:06.779751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.762553Z","title":"Denoising di ffusion probabilistic models","venue":null,"work_id":"4230858a-b315-45f8-8ff4-c5e3cbc5996d","year":2020},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.306202Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:ff7ae4f25501e6c25c01efc89da4bd5bcc2ff2358bfa155151be8658e304328a","observation_id":"42b6e0ac-9515-49fa-bdb0-5fa53f955f9d","resolution":{"observed_at":"2026-08-09T16:19:06.767278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.08535","last_updated":"2022-05-17T17:59:19Z","snapshot_observed_at":"2026-08-09T03:41:49.677248Z","submitted_at":"2022-05-17T17:59:19Z","title":"AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.08535","snapshot_observed_at":"2026-08-09T16:19:06.309916Z","title":"Avatarclip: Zero-shot text-driven generation and animation of 3d avatars","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.309916Z"},"links":{"cited_paper":"/paper/2205.08535","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:f989e7e4b69941d64f85334622641732dfb74e0f0b2419a505b3fc55ebb4e54f","observation_id":"a506ba2a-187b-4e7c-933c-e0af4a003e7e","resolution":{"observed_at":"2026-08-09T16:19:06.309916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.15868","last_updated":"2022-05-29T19:02:15Z","snapshot_observed_at":"2026-07-06T13:15:58.303738Z","submitted_at":"2022-05-29T19:02:15Z","title":"CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.15868","snapshot_observed_at":"2026-08-09T16:19:06.314133Z","title":"Cogvideo: Large-scale pretraining for text-to-video generation via transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.314133Z"},"links":{"cited_paper":"/paper/2205.15868","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:635d6e483399e21cc67c12381d23779d200ad744a39d92d15de53b51e0ebd0b6","observation_id":"0f7469d6-5db3-4a44-8ac6-15ce3b6fd528","resolution":{"observed_at":"2026-08-09T16:19:06.314133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.06119","last_updated":"2023-09-09T13:31:44Z","snapshot_observed_at":"2026-08-04T07:17:35.325005Z","submitted_at":"2020-06-11T00:08:25Z","title":"Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.06119","snapshot_observed_at":"2026-08-09T16:19:06.318097Z","title":"Dance revolution: Long-term dance generation with music via curriculum learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.318097Z"},"links":{"cited_paper":"/paper/2006.06119","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:6355e23b59ba977348963bb50e760cf71d67b3d6101e6520e968571d1df15553","observation_id":"907b3258-d98c-45a1-927e-1738083da7e6","resolution":{"observed_at":"2026-08-09T16:19:06.318097Z","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":"2026-08-09T16:19:06.751714Z","title":"A brand new dance partner: Music-conditioned pluralistic dancing controlled by mult iple dance genres","venue":null,"work_id":"fdb10a33-1b4f-4959-8881-eccd4618909e","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.325443Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:597f7b1de266ee9cfd6201b8b9eb0ba5cf06de6d1fe249e0a75178e20e0cecb5","observation_id":"56d88966-66da-4519-873f-e3b90f26cdfd","resolution":{"observed_at":"2026-08-09T16:19:06.755498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.740749Z","title":"Afi-gan: Improving fea ture interpolation of feature pyramid networks via adversarial training for object detection","venue":null,"work_id":"27406884-d31a-4fdb-ba24-2e4fe2e51c30","year":2023},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.328840Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:2ce0402e10ce32bc0a3d95c942517ea00f6126fcc8186cf966acf0d8bbb90ba5","observation_id":"2aae6eae-f447-465e-b118-55dbd9ef8cfc","resolution":{"observed_at":"2026-08-09T16:19:06.744960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.729364Z","title":"Lodge: A coarse to fine diffusion network for long dance generation g uided by the characteristic dance primitives","venue":null,"work_id":"96732795-6498-4a72-acea-32dfd09459dc","year":2024},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.332322Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:268222aedcfacf9070ce0f8214504b8cb5e0dcc9a3a06a1af2648426305a0998","observation_id":"9d53a074-8b79-44be-822c-6e2c95a0d26f","resolution":{"observed_at":"2026-08-09T16:19:06.734075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.718791Z","title":"Finedance: A fine-grained choreography dataset for 3d full b ody dance generation","venue":null,"work_id":"599a9fb9-6a79-433d-ab94-dc61bd2d068b","year":2023},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.336199Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:a918d171d66a2b1ba325be2a9a0a337a4c84d52d8ac19de07e2bc92a93bcda53","observation_id":"8cc7be8e-7782-4d97-a107-d007bc5332f0","resolution":{"observed_at":"2026-08-09T16:19:06.722594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.707951Z","title":"Ai choreographer: Music conditioned 3d dance generation with aist++","venue":null,"work_id":"3a4fe24c-44b3-40e5-bff9-35a978000ea7","year":2021},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.339747Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:0dc8caf361f76fc466e892976b81b14de23bcbe1cb607a25ac270642bc29020d","observation_id":"038cebae-e9d7-4562-a0e5-0a65b2325298","resolution":{"observed_at":"2026-08-09T16:19:06.711784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.697395Z","title":"Mvitv2: Improved multiscale vision transformers for classification and detection","venue":null,"work_id":"593038a6-3f3d-4632-85f3-9a52056b6658","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.343273Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:68ebc7836e28e00f1448780d0d862da4c9189a7cd97d488ae961af54c4c72e27","observation_id":"1a1a444a-6d26-4557-b05f-0679389b18e8","resolution":{"observed_at":"2026-08-09T16:19:06.701235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.685958Z","title":"Video swin transformer","venue":null,"work_id":"71f18d73-0449-4b11-9f67-fb297f88a393","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.347058Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:7651201ac36a24aee0fd6c62f15e72512ea144f7401f16ccb396a5d4f9423e0f","observation_id":"bceebcd3-e211-4926-8efd-e32db6d6426e","resolution":{"observed_at":"2026-08-09T16:19:06.690378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.03048","last_updated":"2025-05-01T09:40:21Z","snapshot_observed_at":"2026-08-02T13:01:06.918463Z","submitted_at":"2024-01-05T19:55:15Z","title":"Latte: Latent Diffusion Transformer for Video Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.03048","snapshot_observed_at":"2026-08-09T16:19:06.350840Z","title":"Latte: Latent diffusion transformer for video generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.350840Z"},"links":{"cited_paper":"/paper/2401.03048","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:cc853553cf733006e8306c353dc5f6cada12fc89aff91b9cd396e1b38e0f3cbd","observation_id":"5a39e78c-8c3b-4870-9888-dacfaebc7971","resolution":{"observed_at":"2026-08-09T16:19:06.350840Z","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":"2026-08-09T16:19:06.673611Z","title":"librosa: Audio and music signal analysis in python","venue":null,"work_id":"7bd3203c-e3d8-4ea1-b66e-f5e55c9e97ad","year":2015},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.355144Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:fa2e30e35f5f69d28869ec4fb1b7cf6d13678756d1e2918b9b5964932735b9e0","observation_id":"300c20d2-393d-46fd-b8e6-e457347f2e07","resolution":{"observed_at":"2026-08-09T16:19:06.677963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.661655Z","title":"Learn2dance: Learning statistical music-to-dance mappings for choreography synthesis","venue":null,"work_id":"c3f265a4-387f-42e8-8e55-d40159e40212","year":2011},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.358810Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:d7f47bd86d16444843e2842959b1f593d5cce53056c79f57562f0d9e2839b70b","observation_id":"082eb968-59f0-4c2d-8966-4d1769d3c4ce","resolution":{"observed_at":"2026-08-09T16:19:06.665850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.648091Z","title":"Temos : Generating diverse human motions from textual descriptions","venue":null,"work_id":"f74267d6-ab91-4248-b43a-50066cd302c2","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.362492Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:72b1ef17376ed186d006dcbea0f381b4ba6544f025107da8a038e5ef1f374a53","observation_id":"362dd335-1146-45ec-b7d9-99be8c6d07c5","resolution":{"observed_at":"2026-08-09T16:19:06.653313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.635342Z","title":"Long short-term memory recurrent neural network architectures for large scale acoustic modeling","venue":null,"work_id":"876a6db3-79a9-4dd9-b65c-54919196c3be","year":2014},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.366283Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:944c3d041f8143bf7c4da98889618dc0c6d88e87d3612c2ae31f07845d2e5654","observation_id":"41591729-2ec7-4cba-92b7-0a2c55d571b9","resolution":{"observed_at":"2026-08-09T16:19:06.640373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.05911","last_updated":"2019-11-23T06:36:13Z","snapshot_observed_at":"2026-08-07T23:21:14.604647Z","submitted_at":"2019-11-23T06:36:13Z","title":"Recurrent Neural Networks (RNNs): A gentle Introduction and Overview","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.05911","snapshot_observed_at":"2026-08-09T16:19:06.370165Z","title":"Recurrent neural networks (rnns): A ge ntle introduction and overview","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.370165Z"},"links":{"cited_paper":"/paper/1912.05911","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:65c604e60d22bac3957f430f17c7b1bae4c40943d78ca892470ae2b318d96716","observation_id":"c629b5fc-7d7a-4025-9cdc-91ac68e4cc92","resolution":{"observed_at":"2026-08-09T16:19:06.370165Z","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":"2026-08-09T16:19:06.623138Z","title":"Bailando: 3d dance generation by actor-critic gpt with chor eographic memory","venue":null,"work_id":"229d361b-3bb2-46a9-9744-b027eaf8b01c","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.375548Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:0ca7e6ed541ad4ef29ffb0f3741b8f76531ce5b90d8342037fddadbd1ce30749","observation_id":"081d81ad-91d7-4119-8d24-e76f77c303e5","resolution":{"observed_at":"2026-08-09T16:19:06.627113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.379232Z","title":"Denoisi ng diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.379232Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:ae6938000b057ab910c49d2dad9a08741e5dab25e164f91c87d84fa5bf6d7fc0","observation_id":"23ebe1d5-e9fd-4049-be84-a81bb4073ce5","resolution":{"observed_at":"2026-08-09T16:19:06.379232Z","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":"2026-08-09T16:19:06.610583Z","title":"Motionclip: Exposing human motion generation to clip space","venue":null,"work_id":"16ec8036-1c3d-467c-9923-d90412370104","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.383418Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:17722c3d1e5af859beb4e1b65d6f329520eeb0f0b9621ceb360458f30a044363","observation_id":"e555471e-2a04-4c09-a298-1dac5510e69f","resolution":{"observed_at":"2026-08-09T16:19:06.614958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14916","last_updated":"2022-10-03T09:17:41Z","snapshot_observed_at":"2026-07-06T13:57:50.746457Z","submitted_at":"2022-09-29T16:27:53Z","title":"Human Motion Diffusion Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14916","snapshot_observed_at":"2026-08-09T16:19:06.387271Z","title":"Human motion diffusion model","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.387271Z"},"links":{"cited_paper":"/paper/2209.14916","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:7376d48e90fc3844867caf113650fff7106ef05c97fab8b863d8514d5f37f776","observation_id":"be1b5025-9eaa-4756-8f26-18882b504fa9","resolution":{"observed_at":"2026-08-09T16:19:06.387271Z","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":"2026-08-09T16:19:06.598130Z","title":"Edge : Editable dance generation from music","venue":null,"work_id":"95b333dd-d70b-43b6-9e2a-397051a407aa","year":2023},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.391324Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:7dd5312fc7b48f84dc9253b1a039db9a0b9377d8b53f19001ffd35cc28e36d07","observation_id":"d43caadb-d9f9-47ce-9b29-bd5478aa1abb","resolution":{"observed_at":"2026-08-09T16:19:06.602099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.585202Z","title":"Transflower: probabilistic autoregressive dance generation with multimodal attention","venue":null,"work_id":"ad11890e-2a73-4d73-9044-7a558564612f","year":2021},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.394919Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:b17ca766378239834feff28d1e4056688f7c0d38128da21bce0ea01bb761e174","observation_id":"cdf95fc0-544c-4505-b1f8-13ff9e2eb1da","resolution":{"observed_at":"2026-08-09T16:19:06.590104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.10157","last_updated":"2021-09-14T21:20:06Z","snapshot_observed_at":"2026-07-06T11:02:11.424356Z","submitted_at":"2021-04-20T17:58:03Z","title":"VideoGPT: Video Generation using VQ-VAE and Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.10157","snapshot_observed_at":"2026-08-09T16:19:06.398816Z","title":"Videogpt: Video generation using vq-vae and transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.398816Z"},"links":{"cited_paper":"/paper/2104.10157","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:b92d007167cbf0157cdedffdaf964f00d9e211935d4ba382904b77a8d6ef9ccb","observation_id":"5a2ffec0-a619-41c4-88f6-7ab5bf3475e1","resolution":{"observed_at":"2026-08-09T16:19:06.398816Z","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":"2026-08-09T16:19:06.573314Z","title":"Magvit: Masked gener ative video transformer","venue":null,"work_id":"8ddca635-cd60-454b-bdcd-d30c27bf1f92","year":2023},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.402898Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:b1aef98551add38a1c057a70230608bbd2e27885461ecf2b67b78cc1c51b79f3","observation_id":"4e92ec0a-a9f7-483b-a405-3754332f5adb","resolution":{"observed_at":"2026-08-09T16:19:06.577206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.06052","last_updated":"2023-09-24T17:00:32Z","snapshot_observed_at":"2026-08-05T06:24:11.602674Z","submitted_at":"2023-01-15T09:34:42Z","title":"T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.06052","snapshot_observed_at":"2026-08-09T16:19:06.406876Z","title":"T2m-gpt: Generating human motion from textual desc riptions with discrete representations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.406876Z"},"links":{"cited_paper":"/paper/2301.06052","citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:7247a11a25d4bf3783f1fb63a4f68b9a9583e2d22eaef20213bd9aef10f405a2","observation_id":"b22d27d9-f398-4c68-b278-f494e15c8913","resolution":{"observed_at":"2026-08-09T16:19:06.406876Z","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":"2026-08-09T16:19:06.561308Z","title":"Dance dance generation: Motion transfer for internet videos","venue":null,"work_id":"f5644b7a-d84f-4b1f-806b-d9fceedd64df","year":2019},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.410813Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:b58b183b111c3a78be9fd01318d2af5ce86a9675baa575332e69f4fe6120d968","observation_id":"1719a1cd-d362-4f93-a797-e1e1e0d209cd","resolution":{"observed_at":"2026-08-09T16:19:06.565872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T16:19:06.545583Z","title":"Music2dance: Dancenet for music-driven dance generation","venue":null,"work_id":"e1b90445-52f0-4a4c-8bda-25a16a6efbeb","year":2022},"citing_paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T16:19:06.414648Z"},"links":{"citing_paper":"/paper/2502.01190"},"observation_digest":"sha256:ce6fa7cc61a5d9bd6ad0e974e9f6ba2ab247eca41b8ca2672e5af86027e9e1a7","observation_id":"325dd852-fd0e-4636-b3d7-189b51ccfe50","resolution":{"observed_at":"2026-08-09T16:19:06.552686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.01190","last_updated":"2025-02-03T09:29:02Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T12:42:29.873951Z","submitted_at":"2025-02-03T09:29:02Z","title":"Dance recalibration for dance coherency with recurrent convolution block"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":40},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2502.01190."}