{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2ZZJXZLHASTY7R2N5TZATIP5OL","short_pith_number":"pith:2ZZJXZLH","schema_version":"1.0","canonical_sha256":"d6729be56704a78fc74decf209a1fd72e18a3ace2a2d17fb18583461236e114b","source":{"kind":"arxiv","id":"2403.10731","version":2},"attestation_state":"computed","paper":{"title":"Giving a Hand to Diffusion Models: a Two-Stage Approach to Improving Conditional Human Image Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Anton Pelykh, Ozge Mercanoglu Sincan, Richard Bowden","submitted_at":"2024-03-15T23:31:41Z","abstract_excerpt":"Recent years have seen significant progress in human image generation, particularly with the advancements in diffusion models. However, existing diffusion methods encounter challenges when producing consistent hand anatomy and the generated images often lack precise control over the hand pose. To address this limitation, we introduce a novel approach to pose-conditioned human image generation, dividing the process into two stages: hand generation and subsequent body outpainting around the hands. We propose training the hand generator in a multi-task setting to produce both hand images and thei"},"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":"2403.10731","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-15T23:31:41Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"da157339ab3a55272daae174a2cd081fa582e70d34eb739be79f16eb422d8813","abstract_canon_sha256":"4ec771f375d3891bf9cc28552a90a10300ea9b49bb2e4f3192214548b868092c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:31.065665Z","signature_b64":"kWNEM4shVbbE0BAPZeN+j1IlfjoRoAjl0Rz1la9byml1bcXGRrD9MftqQve3fjiUx4vH4frN6NHq+mpZQpZgCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6729be56704a78fc74decf209a1fd72e18a3ace2a2d17fb18583461236e114b","last_reissued_at":"2026-07-05T08:13:31.065123Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:31.065123Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Giving a Hand to Diffusion Models: a Two-Stage Approach to Improving Conditional Human Image Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Anton Pelykh, Ozge Mercanoglu Sincan, Richard Bowden","submitted_at":"2024-03-15T23:31:41Z","abstract_excerpt":"Recent years have seen significant progress in human image generation, particularly with the advancements in diffusion models. However, existing diffusion methods encounter challenges when producing consistent hand anatomy and the generated images often lack precise control over the hand pose. To address this limitation, we introduce a novel approach to pose-conditioned human image generation, dividing the process into two stages: hand generation and subsequent body outpainting around the hands. We propose training the hand generator in a multi-task setting to produce both hand images and thei"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.10731","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/2403.10731/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":"2403.10731","created_at":"2026-07-05T08:13:31.065192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.10731v2","created_at":"2026-07-05T08:13:31.065192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.10731","created_at":"2026-07-05T08:13:31.065192+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZZJXZLHASTY","created_at":"2026-07-05T08:13:31.065192+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZZJXZLHASTY7R2N","created_at":"2026-07-05T08:13:31.065192+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZZJXZLH","created_at":"2026-07-05T08:13:31.065192+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.04725","citing_title":"Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL","json":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL.json","graph_json":"https://pith.science/api/pith-number/2ZZJXZLHASTY7R2N5TZATIP5OL/graph.json","events_json":"https://pith.science/api/pith-number/2ZZJXZLHASTY7R2N5TZATIP5OL/events.json","paper":"https://pith.science/paper/2ZZJXZLH"},"agent_actions":{"view_html":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL","download_json":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL.json","view_paper":"https://pith.science/paper/2ZZJXZLH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.10731&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZZJXZLHASTY7R2N5TZATIP5OL/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZZJXZLHASTY7R2N5TZATIP5OL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL/action/storage_attestation","attest_author":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL/action/author_attestation","sign_citation":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL/action/citation_signature","submit_replication":"https://pith.science/pith/2ZZJXZLHASTY7R2N5TZATIP5OL/action/replication_record"}},"created_at":"2026-07-05T08:13:31.065192+00:00","updated_at":"2026-07-05T08:13:31.065192+00:00"}