{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KKLEIMXAYSOTAAWWPXRLIHTNRI","short_pith_number":"pith:KKLEIMXA","schema_version":"1.0","canonical_sha256":"52964432e0c49d3002d67de2b41e6d8a01533ff7e814f7fcb66b45800ccdb30c","source":{"kind":"arxiv","id":"2311.16818","version":1},"attestation_state":"computed","paper":{"title":"DI-Net : Decomposed Implicit Garment Transfer Network for Digital Clothed 3D Human","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guosheng Lin, Qingyao Wu, Xiaojing Zhong, Yukun Su, Zhonghua Wu","submitted_at":"2023-11-28T14:28:41Z","abstract_excerpt":"3D virtual try-on enjoys many potential applications and hence has attracted wide attention. However, it remains a challenging task that has not been adequately solved. Existing 2D virtual try-on methods cannot be directly extended to 3D since they lack the ability to perceive the depth of each pixel. Besides, 3D virtual try-on approaches are mostly built on the fixed topological structure and with heavy computation. To deal with these problems, we propose a Decomposed Implicit garment transfer network (DI-Net), which can effortlessly reconstruct a 3D human mesh with the newly try-on result an"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2311.16818","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-28T14:28:41Z","cross_cats_sorted":[],"title_canon_sha256":"6a80866a378c857158f7874b5b6224444f1138abe51b64f3b423a8332b47d046","abstract_canon_sha256":"837e3875c467529e12b7061cdf9b96770c2ac1d2efca05424998fad129bd8e10"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:46.865169Z","signature_b64":"RtC7D8NrhyKq4r6rUQd/zWXHQKxj+eostTCC5VXsyfiGbK5OWwSsjjfHTW/g7l0LMS2sBY8BG0kLTBX4f4wbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52964432e0c49d3002d67de2b41e6d8a01533ff7e814f7fcb66b45800ccdb30c","last_reissued_at":"2026-07-05T07:17:46.864682Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:46.864682Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DI-Net : Decomposed Implicit Garment Transfer Network for Digital Clothed 3D Human","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guosheng Lin, Qingyao Wu, Xiaojing Zhong, Yukun Su, Zhonghua Wu","submitted_at":"2023-11-28T14:28:41Z","abstract_excerpt":"3D virtual try-on enjoys many potential applications and hence has attracted wide attention. However, it remains a challenging task that has not been adequately solved. Existing 2D virtual try-on methods cannot be directly extended to 3D since they lack the ability to perceive the depth of each pixel. Besides, 3D virtual try-on approaches are mostly built on the fixed topological structure and with heavy computation. To deal with these problems, we propose a Decomposed Implicit garment transfer network (DI-Net), which can effortlessly reconstruct a 3D human mesh with the newly try-on result an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16818","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.16818/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2311.16818","created_at":"2026-07-05T07:17:46.864747+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.16818v1","created_at":"2026-07-05T07:17:46.864747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16818","created_at":"2026-07-05T07:17:46.864747+00:00"},{"alias_kind":"pith_short_12","alias_value":"KKLEIMXAYSOT","created_at":"2026-07-05T07:17:46.864747+00:00"},{"alias_kind":"pith_short_16","alias_value":"KKLEIMXAYSOTAAWW","created_at":"2026-07-05T07:17:46.864747+00:00"},{"alias_kind":"pith_short_8","alias_value":"KKLEIMXA","created_at":"2026-07-05T07:17:46.864747+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15616","citing_title":"IPVTON: Image-based 3D Virtual Try-on with Image Prompt Adapter","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI","json":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI.json","graph_json":"https://pith.science/api/pith-number/KKLEIMXAYSOTAAWWPXRLIHTNRI/graph.json","events_json":"https://pith.science/api/pith-number/KKLEIMXAYSOTAAWWPXRLIHTNRI/events.json","paper":"https://pith.science/paper/KKLEIMXA"},"agent_actions":{"view_html":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI","download_json":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI.json","view_paper":"https://pith.science/paper/KKLEIMXA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.16818&json=true","fetch_graph":"https://pith.science/api/pith-number/KKLEIMXAYSOTAAWWPXRLIHTNRI/graph.json","fetch_events":"https://pith.science/api/pith-number/KKLEIMXAYSOTAAWWPXRLIHTNRI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI/action/storage_attestation","attest_author":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI/action/author_attestation","sign_citation":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI/action/citation_signature","submit_replication":"https://pith.science/pith/KKLEIMXAYSOTAAWWPXRLIHTNRI/action/replication_record"}},"created_at":"2026-07-05T07:17:46.864747+00:00","updated_at":"2026-07-05T07:17:46.864747+00:00"}