{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ROSIS2NVZCJTDVDZ2RXHR7PFEC","short_pith_number":"pith:ROSIS2NV","schema_version":"1.0","canonical_sha256":"8ba48969b5c89331d479d46e78fde520920e4a72cd8172bafbb3f5e4b7b2dc06","source":{"kind":"arxiv","id":"2405.04675","version":1},"attestation_state":"computed","paper":{"title":"TexControl: Sketch-Based Two-Stage Fashion Image Generation Using Diffusion Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Haoran Xie, Tianyu Zhang, Yongming Zhang","submitted_at":"2024-05-07T21:18:34Z","abstract_excerpt":"Deep learning-based sketch-to-clothing image generation provides the initial designs and inspiration in the fashion design processes. However, clothing generation from freehand drawing is challenging due to the sparse and ambiguous information from the drawn sketches. The current generation models may have difficulty generating detailed texture information. In this work, we propose TexControl, a sketch-based fashion generation framework that uses a two-stage pipeline to generate the fashion image corresponding to the sketch input. First, we adopt ControlNet to generate the fashion image from s"},"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":"2405.04675","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-07T21:18:34Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"bf7407be96a1f431c5440e94112a79c468f35de395bcbcd75c9b4c6ab2877b03","abstract_canon_sha256":"e43d6d8bf7ba4e3a94aa407748c55970e75a367f207ada50c51fa75ff6d2a35e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:52.369973Z","signature_b64":"DYkWWqK71hpLBeaUJv550agNTaYNBlH1VZgo40J2LHu3nY2wS6T8M/C3HqWmZK/vc6tMpZnObSLM/0J14iuWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ba48969b5c89331d479d46e78fde520920e4a72cd8172bafbb3f5e4b7b2dc06","last_reissued_at":"2026-07-05T08:16:52.367490Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:52.367490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TexControl: Sketch-Based Two-Stage Fashion Image Generation Using Diffusion Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Haoran Xie, Tianyu Zhang, Yongming Zhang","submitted_at":"2024-05-07T21:18:34Z","abstract_excerpt":"Deep learning-based sketch-to-clothing image generation provides the initial designs and inspiration in the fashion design processes. However, clothing generation from freehand drawing is challenging due to the sparse and ambiguous information from the drawn sketches. The current generation models may have difficulty generating detailed texture information. In this work, we propose TexControl, a sketch-based fashion generation framework that uses a two-stage pipeline to generate the fashion image corresponding to the sketch input. First, we adopt ControlNet to generate the fashion image from s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04675","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/2405.04675/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":"2405.04675","created_at":"2026-07-05T08:16:52.369119+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04675v1","created_at":"2026-07-05T08:16:52.369119+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04675","created_at":"2026-07-05T08:16:52.369119+00:00"},{"alias_kind":"pith_short_12","alias_value":"ROSIS2NVZCJT","created_at":"2026-07-05T08:16:52.369119+00:00"},{"alias_kind":"pith_short_16","alias_value":"ROSIS2NVZCJTDVDZ","created_at":"2026-07-05T08:16:52.369119+00:00"},{"alias_kind":"pith_short_8","alias_value":"ROSIS2NV","created_at":"2026-07-05T08:16:52.369119+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.23186","citing_title":"HiGarment: Cross-modal Harmony Based Diffusion Model for Flat Sketch to Realistic Garment Image","ref_index":37,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC","json":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC.json","graph_json":"https://pith.science/api/pith-number/ROSIS2NVZCJTDVDZ2RXHR7PFEC/graph.json","events_json":"https://pith.science/api/pith-number/ROSIS2NVZCJTDVDZ2RXHR7PFEC/events.json","paper":"https://pith.science/paper/ROSIS2NV"},"agent_actions":{"view_html":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC","download_json":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC.json","view_paper":"https://pith.science/paper/ROSIS2NV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04675&json=true","fetch_graph":"https://pith.science/api/pith-number/ROSIS2NVZCJTDVDZ2RXHR7PFEC/graph.json","fetch_events":"https://pith.science/api/pith-number/ROSIS2NVZCJTDVDZ2RXHR7PFEC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC/action/storage_attestation","attest_author":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC/action/author_attestation","sign_citation":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC/action/citation_signature","submit_replication":"https://pith.science/pith/ROSIS2NVZCJTDVDZ2RXHR7PFEC/action/replication_record"}},"created_at":"2026-07-05T08:16:52.369119+00:00","updated_at":"2026-07-05T08:16:52.369119+00:00"}