{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YSV7ZMEWAFR6F5GIYORNXXLPOG","short_pith_number":"pith:YSV7ZMEW","schema_version":"1.0","canonical_sha256":"c4abfcb0960163e2f4c8c3a2dbdd6f7180785742cc40cfa564d0731ef2b828b2","source":{"kind":"arxiv","id":"2311.10329","version":5},"attestation_state":"computed","paper":{"title":"High-fidelity Person-centric Subject-to-Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Cheng Jin, Jianwei Zheng, Weizhong Zhang, Yibin Wang","submitted_at":"2023-11-17T05:03:53Z","abstract_excerpt":"Current subject-driven image generation methods encounter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion, which involves an irreconcilable training imbalance. Precisely, to generate realistic persons, they need to sufficiently tune the pre-trained model, which inevitably causes the model to forget the rich semantic scene prior and makes scene generation over-fit to the training data. Moreover, even with sufficient fine-tuning, these methods can still not generate hig"},"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":"2311.10329","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-17T05:03:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6f4a8142070b2b83a371675d19c0e2b87fadfacb2fe9cf3331932a3768370b81","abstract_canon_sha256":"12c76303bb076a479ddf363528ab86c3babb121bf84b319e2250f631f989ad33"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:51.636987Z","signature_b64":"laKnxD/MhAiy6WsV1oTjNhTAsQCq3YojH/43tTWvJCBpCHO5WiESzeqwgU1t5Uq10PbEFY1SN3ukbAKMPACBDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4abfcb0960163e2f4c8c3a2dbdd6f7180785742cc40cfa564d0731ef2b828b2","last_reissued_at":"2026-07-05T08:14:51.636568Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:51.636568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High-fidelity Person-centric Subject-to-Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Cheng Jin, Jianwei Zheng, Weizhong Zhang, Yibin Wang","submitted_at":"2023-11-17T05:03:53Z","abstract_excerpt":"Current subject-driven image generation methods encounter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion, which involves an irreconcilable training imbalance. Precisely, to generate realistic persons, they need to sufficiently tune the pre-trained model, which inevitably causes the model to forget the rich semantic scene prior and makes scene generation over-fit to the training data. Moreover, even with sufficient fine-tuning, these methods can still not generate hig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.10329","kind":"arxiv","version":5},"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/2311.10329/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":"2311.10329","created_at":"2026-07-05T08:14:51.636622+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.10329v5","created_at":"2026-07-05T08:14:51.636622+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.10329","created_at":"2026-07-05T08:14:51.636622+00:00"},{"alias_kind":"pith_short_12","alias_value":"YSV7ZMEWAFR6","created_at":"2026-07-05T08:14:51.636622+00:00"},{"alias_kind":"pith_short_16","alias_value":"YSV7ZMEWAFR6F5GI","created_at":"2026-07-05T08:14:51.636622+00:00"},{"alias_kind":"pith_short_8","alias_value":"YSV7ZMEW","created_at":"2026-07-05T08:14:51.636622+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG","json":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG.json","graph_json":"https://pith.science/api/pith-number/YSV7ZMEWAFR6F5GIYORNXXLPOG/graph.json","events_json":"https://pith.science/api/pith-number/YSV7ZMEWAFR6F5GIYORNXXLPOG/events.json","paper":"https://pith.science/paper/YSV7ZMEW"},"agent_actions":{"view_html":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG","download_json":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG.json","view_paper":"https://pith.science/paper/YSV7ZMEW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.10329&json=true","fetch_graph":"https://pith.science/api/pith-number/YSV7ZMEWAFR6F5GIYORNXXLPOG/graph.json","fetch_events":"https://pith.science/api/pith-number/YSV7ZMEWAFR6F5GIYORNXXLPOG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG/action/storage_attestation","attest_author":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG/action/author_attestation","sign_citation":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG/action/citation_signature","submit_replication":"https://pith.science/pith/YSV7ZMEWAFR6F5GIYORNXXLPOG/action/replication_record"}},"created_at":"2026-07-05T08:14:51.636622+00:00","updated_at":"2026-07-05T08:14:51.636622+00:00"}