{"as_of":"2026-08-21T17:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ad2429b66fbc1d245a2b87a98e341c0ac225f9b550b09825025a46636ee9863f","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:00:23.977030Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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":"2026-08-06T17:01:56.448712Z","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":"2026-08-06T17:01:57.253188Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"cited_work":{"arxiv_id":"2412.11435","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.11435","snapshot_observed_at":"2026-08-06T17:01:57.253188Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","venue":"cs.CV","work_id":"bece7d31-abdb-4127-b7b7-d71cb26fa0be","year":2024},"citing_paper":{"arxiv_id":"2507.12062","last_updated":"2025-07-16T09:18:18Z","snapshot_observed_at":"2026-08-17T21:34:15.122198Z","submitted_at":"2025-07-16T09:18:18Z","title":"MS-DETR: Towards Effective Video Moment Retrieval and Highlight Detection by Joint Motion-Semantic Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T17:01:56.448712Z"},"links":{"cited_paper":"/paper/2412.11435","citing_paper":"/paper/2507.12062"},"observation_digest":"sha256:70ff511d0cfeb4c4238708374f1c52ba900bea3ef63855c627f526cdab9d8fa4","observation_id":"057d5f86-c1a8-46e1-8c78-10533682f541","resolution":{"observed_at":"2026-08-06T17:01:57.336574Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.11435/citation-record","integrity":"/paper/2412.11435/integrity","json":"/paper/2412.11435/citation-record.json","paper":"/paper/2412.11435"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:00:24.412992Z","title":"Stable diffusion 2.1","venue":null,"work_id":"8e7335a1-0b99-4b30-82de-c35dd66f4d5b","year":2023},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.842077Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:1f581e54acaed857751f9479bca24ed0f34fbc7af21ad122ebcfe73bc84351f0","observation_id":"48533a8e-9ed9-4b9a-8fae-9f49eafa7ff0","resolution":{"observed_at":"2026-08-11T15:00:24.416867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01401","last_updated":"2021-01-14T05:36:59Z","snapshot_observed_at":"2026-08-13T06:40:48.610500Z","submitted_at":"2018-01-04T15:25:26Z","title":"Demystifying MMD GANs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01401","snapshot_observed_at":"2026-08-11T15:00:23.847600Z","title":"Demystifying mmd gans","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.847600Z"},"links":{"cited_paper":"/paper/1801.01401","citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:2e14c252359c7e515d7eae3719555f6df068949cc615085e428ab7bead87333e","observation_id":"d28668c7-ad68-40be-9126-4ee553cf37d5","resolution":{"observed_at":"2026-08-11T15:00:23.847600Z","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":"2026-08-11T15:00:24.400382Z","title":"Realtime multi-person 2d pose estimation using part affinity fields","venue":null,"work_id":"0ce24e0a-6c1f-46c7-8af5-5cd7c33e1ace","year":2017},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.852950Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:50e77613d15d4bb7b355825620a7fe08050ebb4996c4ad36353c9a545858b516","observation_id":"82388b30-b03c-4427-90c4-a433aea41e27","resolution":{"observed_at":"2026-08-11T15:00:24.404778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.387193Z","title":"Viton-hd: High-resolution virtual try-on via misalignment-aware normalization","venue":null,"work_id":"c9495eb1-bbfe-4ffe-965f-4806f42f644f","year":2021},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.858271Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:388d3ab0e3dcbdc01e12c21e72d0f17f59626461d6c6bd9b3b883d2a8d06cb04","observation_id":"0b18325f-e6a2-4353-96b5-0dbc12e1e7ed","resolution":{"observed_at":"2026-08-11T15:00:24.391654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05139","last_updated":"2024-07-29T09:15:33Z","snapshot_observed_at":"2026-08-20T21:37:11.210042Z","submitted_at":"2024-03-08T08:12:18Z","title":"Improving Diffusion Models for Authentic Virtual Try-on in the Wild","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05139","snapshot_observed_at":"2026-08-11T15:00:23.863468Z","title":"Improving diffusion models for vir- tual try-on","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.863468Z"},"links":{"cited_paper":"/paper/2403.05139","citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:481467b5eded91dda39d6df75d26e2fd3479f52f4a0715057597cc61062a19dd","observation_id":"2ddcda41-83d8-4ce4-a19f-ba3cb9f987f8","resolution":{"observed_at":"2026-08-11T15:00:23.863468Z","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":"2026-08-11T15:00:24.370636Z","title":"Catvton: Concatenation is all you need for virtual try-on with diffusion models, 2024","venue":null,"work_id":"1d065540-7186-4cae-9020-50b86feb7d66","year":2024},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.868992Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:c8f9dadbe7651c2ee7406af916af7a84344511401a3f1702cba8e174d84879ef","observation_id":"36460f83-f178-49cb-a5ef-c1a14089bcfb","resolution":{"observed_at":"2026-08-11T15:00:24.376635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.354823Z","title":"Diffusion mod- els beat gans on image synthesis","venue":null,"work_id":"cb6c0b68-0ede-435e-9644-d6bbb9a2cf84","year":2021},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.875351Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:056e6034f582a2cb764925eab41b3175c80a05148b6607a1083ea463c66443a7","observation_id":"890a3eaa-527f-4758-bb1b-b278943416f3","resolution":{"observed_at":"2026-08-11T15:00:24.360274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.338842Z","title":"Exploring Warping-Guided Features via Adaptive Latent Diffusion Model for Virtual try-on","venue":null,"work_id":"04cd2e6b-3619-4140-bcf7-bc5995a88d93","year":2024},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.879639Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:2422791a26b1b5259eb861c92c86448db28f4d9fcbf910d1b2930ec9933d3764","observation_id":"7a639d0c-4550-47d2-9c7c-0a08bcd57ea4","resolution":{"observed_at":"2026-08-11T15:00:24.344143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.322776Z","title":"Parser-free virtual try-on via distilling ap- pearance flows","venue":null,"work_id":"fd3b5870-a398-42b5-8c13-1fa820d5cd8f","year":2021},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.883978Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:5025659858a3645c312163c90ecb0d3657c161de8969e3ca38c922d1a24d5445","observation_id":"ed8567b9-0f44-478b-bf95-7a232f961503","resolution":{"observed_at":"2026-08-11T15:00:24.327679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.306003Z","title":"Taming the power of diffusion models for high-quality virtual try-on with appearance flow","venue":null,"work_id":"b2bf55c8-5cf0-47de-afac-9e84e972fd9c","year":2023},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.888639Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:a72a21c542f0e12dd7259adda058711cc2469ee6a9abcf88dfe74413d18346df","observation_id":"b14f219f-23fe-44cc-b231-8e319b038e6f","resolution":{"observed_at":"2026-08-11T15:00:24.311123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.292267Z","title":"Vec- tor quantized diffusion model for text-to-image synthesis","venue":null,"work_id":"2a9f00cd-dbf7-4b2b-970d-d727204e8b69","year":2022},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.893857Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:4f6029bc02ef0fa4930bbf16554b4a9fd6dd2f7bcb2660d2ed92eef701aa0487","observation_id":"17071b55-c733-4106-93a5-f36b864f7467","resolution":{"observed_at":"2026-08-11T15:00:24.296303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:23.898888Z","title":"Viton: An image-based virtual try-on network","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.898888Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:6199241a90373c4eff2305ad36860933c8d00baf89f1006ccf0a658559094f98","observation_id":"93b7e3b0-32e5-4e16-89de-cbfa1c5ce9f1","resolution":{"observed_at":"2026-08-11T15:00:23.898888Z","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":"2026-08-11T15:00:24.271271Z","title":"Clothflow: A flow-based model for clothed person generation","venue":null,"work_id":"0778b7df-b022-44c2-b5e6-78241df7cb1d","year":2019},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.903619Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:4c2cda261ca37eb05fc02d16f2048d589dd030892e818d8ec7ff02c598098635","observation_id":"cdcb96bd-2bea-4bc0-85ff-d045a00e0a03","resolution":{"observed_at":"2026-08-11T15:00:24.275645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.258068Z","title":"Style-based global appearance flow for virtual try-on","venue":null,"work_id":"c401b4b1-ef02-4858-ba3e-babcf758b6c1","year":2022},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.908118Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:0ccde45e8ea9faedb38c83a84bd0f306774c057b5e106ff3cd0c950f72f55421","observation_id":"daccf6e8-5de0-432e-987a-b71e737291f5","resolution":{"observed_at":"2026-08-11T15:00:24.263072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-08-14T06:37:15.299690Z","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":"2026-08-11T15:00:23.912338Z","title":"Classifier-free diffusion guidance","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.912338Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:511ea1b31e3e37fd205878bb2e123d5e3c13ad4723ee28419de2e440291ff2f4","observation_id":"85210b2a-321c-4f41-ac41-e34059cda34a","resolution":{"observed_at":"2026-08-11T15:00:23.912338Z","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":"2026-08-11T15:00:23.916679Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.916679Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:2e37f9baea492179653f099a1b635d604383dc0348d15e4e4b2e3f6dbd76b000","observation_id":"ea05494e-a82c-4ad8-9ab2-131eb1c8af59","resolution":{"observed_at":"2026-08-11T15:00:23.916679Z","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":"2026-08-11T15:00:24.234144Z","title":"Stableviton: Learning semantic corre- spondence with latent diffusion model for virtual try-on","venue":null,"work_id":"4251e25a-fc7f-464f-986c-ff8101115d7c","year":2024},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.920896Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:a8cef53519d1e35aac7b1dea1a61d21c68b59fd81f01e756ef7cb7b08febfbdb","observation_id":"9ca545f7-95b7-453d-a9c5-34b862a9d5ec","resolution":{"observed_at":"2026-08-11T15:00:24.238702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.221452Z","title":"High-resolution virtual try-on with misalignment and occlusion-handled conditions","venue":null,"work_id":"56bcaa84-f717-4461-8d40-bdabfe58fc68","year":2022},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.924858Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:6e63522b918cb88b97dea321c3307949ea77441db10b1c2f10564f5151cbaddb","observation_id":"b149dd92-3caa-4d8f-a575-32485de99965","resolution":{"observed_at":"2026-08-11T15:00:24.225697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.208227Z","title":"Toward accurate and realistic outfits visualization with atten- tion to details","venue":null,"work_id":"b256a299-ea75-45df-a22c-e7f9fe7ce440","year":2021},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.928432Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:aa704b19b419489d7e068230bfc79bf28726338b656ba92a8f9b54a2bf7fa7f1","observation_id":"ca893e42-0084-4252-ac7a-c13e155c6921","resolution":{"observed_at":"2026-08-11T15:00:24.213322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:23.931810Z","title":"Self- correction for human parsing","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.931810Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:e7cc53f1abafa3fc9f3fa57e4b08ee4c4d979114bab4f205b96e47563d779c98","observation_id":"3613052f-0c16-4f27-82b2-1dd82461d839","resolution":{"observed_at":"2026-08-11T15:00:23.931810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-11T15:00:23.935414Z","title":"Fixing weight decay regularization in adam","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.935414Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:bb85165eff07f0141e23b60e837a0a2fece6f5162c42d41bf6ef887a01f96509","observation_id":"75b06e59-6934-4b1f-8723-e22eb9198542","resolution":{"observed_at":"2026-08-11T15:00:23.935414Z","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":"2026-08-11T15:00:24.183475Z","title":"Dress code: High- resolution multi-category virtual try-on","venue":null,"work_id":"fb14a767-c9b3-49e5-b4e9-a73dbdc59093","year":2022},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.939257Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:ddb58a44714232d33cd856ea861fdb13f2047f4acabc858d4c15c32633d539f2","observation_id":"b4075fe7-137c-43a8-9684-74b87cc3d448","resolution":{"observed_at":"2026-08-11T15:00:24.189771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.169379Z","title":"Ladi-vton: Latent diffusion textual-inversion enhanced virtual try-on","venue":null,"work_id":"14a70384-ff06-4937-bdfb-f612a6d020b5","year":2023},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.943142Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:8556dab569233289043e885d47bca9684f94ae3b9d26db41306c2e02e30e81d1","observation_id":"1115dd83-8e2f-4cf3-b885-707c57082d39","resolution":{"observed_at":"2026-08-11T15:00:24.174052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.156139Z","title":"On aliased resizing and surprising subtleties in gan evaluation","venue":null,"work_id":"1b542923-a0a8-4f9b-8e98-187c6ea33142","year":2022},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.948252Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:705d9c0f3bd8ad48e06ebbe4dee56d4c3a0ca910a014d04d9817d1650e46ba8b","observation_id":"adb9785b-b393-4bb5-bfb5-ab006af68b04","resolution":{"observed_at":"2026-08-11T15:00:24.160789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:23.952186Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.952186Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:9899105a74d18e2545329d64c230009f3897a9583e81b626089fe32c9573ce55","observation_id":"a9e8bafd-332c-4683-9d51-70cdd3a1f646","resolution":{"observed_at":"2026-08-11T15:00:23.952186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.08258","last_updated":"2024-09-12T17:55:11Z","snapshot_observed_at":"2026-08-16T13:19:02.735494Z","submitted_at":"2024-09-12T17:55:11Z","title":"Improving Virtual Try-On with Garment-focused Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.08258","snapshot_observed_at":"2026-08-11T15:00:23.956259Z","title":"Improving virtual try- on with garment-focused diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.956259Z"},"links":{"cited_paper":"/paper/2409.08258","citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:e695a1a4bb616ffd70836364e0effd431ed92c2bf4e208965fc9e86d2778ecb1","observation_id":"831950b0-4457-4434-b64e-2e0d2b990e75","resolution":{"observed_at":"2026-08-11T15:00:23.956259Z","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":"2026-08-11T15:00:24.135407Z","title":"Toward characteristic- preserving image-based virtual try-on network","venue":null,"work_id":"e2ae925a-5a5b-4621-8315-893e96f028b1","year":2018},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.960343Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:b792b23150e24b3727d44a54854f056e6178b784cff9933f9cce3f1563a603cb","observation_id":"ffaef0a4-04b7-4e7e-83d4-22b3f9629286","resolution":{"observed_at":"2026-08-11T15:00:24.139855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:24.121360Z","title":"Gp- vton: Towards general purpose virtual try-on via collabora- tive local-flow global-parsing learning","venue":null,"work_id":"df5fcb62-bf18-4e19-a510-15be72aaf91e","year":2023},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.964812Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:f68a6e1897b7354a6eca7b0c1cbff05ba2d73b1b0e87318c87ac6fe0687849ac","observation_id":"3e4d20cc-6be2-4b87-810c-7a48117f0173","resolution":{"observed_at":"2026-08-11T15:00:24.126558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15111","last_updated":"2024-07-21T10:40:53Z","snapshot_observed_at":"2026-08-18T22:12:45.152136Z","submitted_at":"2024-07-21T10:40:53Z","title":"D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On","version":1},"cited_work":{"arxiv_id":"2407.15111","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.15111","snapshot_observed_at":"2026-08-11T15:00:24.012937Z","title":"D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On","venue":"cs.CV","work_id":"334aefd4-0e77-4bea-b143-fa0b297fcd80","year":2024},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.968585Z"},"links":{"cited_paper":"/paper/2407.15111","citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:b045ee61553fb94b2b1f43e65647154584eabbddb18c96bd1978dd99661fd553","observation_id":"83864dc6-1411-4ca7-82d5-230bb114c24e","resolution":{"observed_at":"2026-08-11T15:00:24.022445Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-11T15:00:23.973201Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.973201Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:9f72f9412ecff4d2713961a82695d5cce3e9e875400dc405d5c0b04508c024d2","observation_id":"ef756fe9-53ad-442e-bb04-206ad3dc2376","resolution":{"observed_at":"2026-08-11T15:00:23.973201Z","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":"2026-08-11T15:00:24.099283Z","title":"Tryondiffusion: A tale of two unets","venue":null,"work_id":"c2f7d3e4-813d-471b-a48f-e65b97e969db","year":2023},"citing_paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T15:00:23.977030Z"},"links":{"citing_paper":"/paper/2412.11435"},"observation_digest":"sha256:797285cecf3b4c58b1cf5dd461921524abbe625de6df412d256c29ad49f8b8c4","observation_id":"154986e1-37f8-48f5-96f5-d3cdf67f040e","resolution":{"observed_at":"2026-08-11T15:00:24.103780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.11435","last_updated":"2024-12-16T04:23:33Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T18:13:19.351232Z","submitted_at":"2024-12-16T04:23:33Z","title":"Learning Implicit Features with Flow Infused Attention for Realistic Virtual Try-On"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":20},"total_outbound_references":31},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2412.11435."}