{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4AKMMBRFJZECZWQBHBD2M7EVET","short_pith_number":"pith:4AKMMBRF","schema_version":"1.0","canonical_sha256":"e014c606254e482cda013847a67c9524f148248fd468f110ffca3613c7b8f1ec","source":{"kind":"arxiv","id":"2412.08573","version":2},"attestation_state":"computed","paper":{"title":"TryOffAnyone: Tiled Cloth Generation from a Dressed Person","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ioannis Xarchakos, Theodoros Koukopoulos","submitted_at":"2024-12-11T17:41:53Z","abstract_excerpt":"The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challenge of generating high-fidelity tiled garment images essential for personalized recommendations, outfit composition, and virtual try-on systems from photos of garments worn by models. Inspired by the success of Latent Diffusion Models (LDMs) in image-to-image translation, we propose a novel approach utilizing a fine-tuned StableDiffusion model. Our method features a streamlined single-stage networ"},"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":"2412.08573","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-11T17:41:53Z","cross_cats_sorted":[],"title_canon_sha256":"683e8c6e7af6be93bcbcbad7a4e61d5939839d4aeca397e191e249984ff62129","abstract_canon_sha256":"ba3d4f3931ff6c95de8a1f5f6c1c5672fe765e05016bb421b46840b0e89c3d2d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:38.059190Z","signature_b64":"rFagxPEukn73W8dZpAmOjjaFpNOxQpe89gRO+Iv9bp8wZxuojJHAVdocu7DugM5x9mwZGMn26bZPzoX1ItjiAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e014c606254e482cda013847a67c9524f148248fd468f110ffca3613c7b8f1ec","last_reissued_at":"2026-07-05T09:56:38.058689Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:38.058689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TryOffAnyone: Tiled Cloth Generation from a Dressed Person","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ioannis Xarchakos, Theodoros Koukopoulos","submitted_at":"2024-12-11T17:41:53Z","abstract_excerpt":"The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challenge of generating high-fidelity tiled garment images essential for personalized recommendations, outfit composition, and virtual try-on systems from photos of garments worn by models. Inspired by the success of Latent Diffusion Models (LDMs) in image-to-image translation, we propose a novel approach utilizing a fine-tuned StableDiffusion model. Our method features a streamlined single-stage networ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08573","kind":"arxiv","version":2},"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/2412.08573/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":"2412.08573","created_at":"2026-07-05T09:56:38.058749+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.08573v2","created_at":"2026-07-05T09:56:38.058749+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08573","created_at":"2026-07-05T09:56:38.058749+00:00"},{"alias_kind":"pith_short_12","alias_value":"4AKMMBRFJZEC","created_at":"2026-07-05T09:56:38.058749+00:00"},{"alias_kind":"pith_short_16","alias_value":"4AKMMBRFJZECZWQB","created_at":"2026-07-05T09:56:38.058749+00:00"},{"alias_kind":"pith_short_8","alias_value":"4AKMMBRF","created_at":"2026-07-05T09:56:38.058749+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27880","citing_title":"OrthoTryOn: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2603.22607","citing_title":"Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12939","citing_title":"DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2505.20275","citing_title":"ImgEdit: A Unified Image Editing Dataset and Benchmark","ref_index":74,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08716","citing_title":"What Matters in Virtual Try-Off? Dual-UNet Diffusion Model For Garment Reconstruction","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET","json":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET.json","graph_json":"https://pith.science/api/pith-number/4AKMMBRFJZECZWQBHBD2M7EVET/graph.json","events_json":"https://pith.science/api/pith-number/4AKMMBRFJZECZWQBHBD2M7EVET/events.json","paper":"https://pith.science/paper/4AKMMBRF"},"agent_actions":{"view_html":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET","download_json":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET.json","view_paper":"https://pith.science/paper/4AKMMBRF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.08573&json=true","fetch_graph":"https://pith.science/api/pith-number/4AKMMBRFJZECZWQBHBD2M7EVET/graph.json","fetch_events":"https://pith.science/api/pith-number/4AKMMBRFJZECZWQBHBD2M7EVET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/action/storage_attestation","attest_author":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/action/author_attestation","sign_citation":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/action/citation_signature","submit_replication":"https://pith.science/pith/4AKMMBRFJZECZWQBHBD2M7EVET/action/replication_record"}},"created_at":"2026-07-05T09:56:38.058749+00:00","updated_at":"2026-07-05T09:56:38.058749+00:00"}