{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LKWC52TP2FZMKQJRO26UTD253B","short_pith_number":"pith:LKWC52TP","schema_version":"1.0","canonical_sha256":"5aac2eea6fd172c5413176bd498f5dd8595c00a94ad4f585f98f6860912e0b60","source":{"kind":"arxiv","id":"2311.16094","version":3},"attestation_state":"computed","paper":{"title":"Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Aiyu Cui, Chang Liu, Jay Mahajan, Preeti Gomathinayagam, Svetlana Lazebnik, Viraj Shah","submitted_at":"2023-11-27T18:59:02Z","abstract_excerpt":"Most virtual try-on research is motivated to serve the fashion business by generating images to demonstrate garments on studio models at a lower cost. However, virtual try-on should be a broader application that also allows customers to visualize garments on themselves using their own casual photos, known as in-the-wild try-on. Unfortunately, the existing methods, which achieve plausible results for studio try-on settings, perform poorly in the in-the-wild context. This is because these methods often require paired images (garment images paired with images of people wearing the same garment) f"},"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":"2311.16094","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-27T18:59:02Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"cfa2796596ae50b733e635c2ae5c8e241869e8fd03d91f6b0b24f7f4f30867ce","abstract_canon_sha256":"87598509db9d3cf3e4cf5b939f7fc31782a13e2403e89c9053ad203b2d32c8b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:49.249716Z","signature_b64":"u/6GcoVnNRfnJmD8TMUsBSdx5uHDOJpqEV+o3A4uUU6qeRgvAJ3sRKiIiBzukvoBx1Hk972PAorHuuR7etqdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5aac2eea6fd172c5413176bd498f5dd8595c00a94ad4f585f98f6860912e0b60","last_reissued_at":"2026-07-05T08:44:49.249279Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:49.249279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Aiyu Cui, Chang Liu, Jay Mahajan, Preeti Gomathinayagam, Svetlana Lazebnik, Viraj Shah","submitted_at":"2023-11-27T18:59:02Z","abstract_excerpt":"Most virtual try-on research is motivated to serve the fashion business by generating images to demonstrate garments on studio models at a lower cost. However, virtual try-on should be a broader application that also allows customers to visualize garments on themselves using their own casual photos, known as in-the-wild try-on. Unfortunately, the existing methods, which achieve plausible results for studio try-on settings, perform poorly in the in-the-wild context. This is because these methods often require paired images (garment images paired with images of people wearing the same garment) f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16094","kind":"arxiv","version":3},"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/2311.16094/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":"2311.16094","created_at":"2026-07-05T08:44:49.249347+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.16094v3","created_at":"2026-07-05T08:44:49.249347+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16094","created_at":"2026-07-05T08:44:49.249347+00:00"},{"alias_kind":"pith_short_12","alias_value":"LKWC52TP2FZM","created_at":"2026-07-05T08:44:49.249347+00:00"},{"alias_kind":"pith_short_16","alias_value":"LKWC52TP2FZMKQJR","created_at":"2026-07-05T08:44:49.249347+00:00"},{"alias_kind":"pith_short_8","alias_value":"LKWC52TP","created_at":"2026-07-05T08:44:49.249347+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.08526","citing_title":"FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B","json":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B.json","graph_json":"https://pith.science/api/pith-number/LKWC52TP2FZMKQJRO26UTD253B/graph.json","events_json":"https://pith.science/api/pith-number/LKWC52TP2FZMKQJRO26UTD253B/events.json","paper":"https://pith.science/paper/LKWC52TP"},"agent_actions":{"view_html":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B","download_json":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B.json","view_paper":"https://pith.science/paper/LKWC52TP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.16094&json=true","fetch_graph":"https://pith.science/api/pith-number/LKWC52TP2FZMKQJRO26UTD253B/graph.json","fetch_events":"https://pith.science/api/pith-number/LKWC52TP2FZMKQJRO26UTD253B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B/action/storage_attestation","attest_author":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B/action/author_attestation","sign_citation":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B/action/citation_signature","submit_replication":"https://pith.science/pith/LKWC52TP2FZMKQJRO26UTD253B/action/replication_record"}},"created_at":"2026-07-05T08:44:49.249347+00:00","updated_at":"2026-07-05T08:44:49.249347+00:00"}