{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3ZOGMKQMJ4A436GDARSFFUB4XC","short_pith_number":"pith:3ZOGMKQM","schema_version":"1.0","canonical_sha256":"de5c662a0c4f01cdf8c3046452d03cb8a51580a701f3e6d733d7338674cf50b1","source":{"kind":"arxiv","id":"2310.06313","version":4},"attestation_state":"computed","paper":{"title":"Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cong Wang, Fei Shen, Hu Ye, Jun Zhang, Wei Yang, Xiao Han","submitted_at":"2023-10-10T05:13:17Z","abstract_excerpt":"Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Progressive Conditional Diffusion Models (PCDMs) that incrementally bridge the gap between person images under the target and source poses through three stages. Specifically, in the first stage, we design a simple prior conditional diffusion "},"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":"2310.06313","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-10T05:13:17Z","cross_cats_sorted":[],"title_canon_sha256":"615c431523968889e46e5a54f22f54a49d3f19a60b3ec3cb560936011393f283","abstract_canon_sha256":"d12f41d1f49a7962299028091baf7d54545ad889a213557c33028bd30f338780"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:22.046582Z","signature_b64":"GuWIn8rcdDOlnDLIcq6VuiTQcxcOHScOocLArTkzOjDYNVm3R/rRGhSDhb0iGDMs2Q1Qnta54fpyxMZkVRIJAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de5c662a0c4f01cdf8c3046452d03cb8a51580a701f3e6d733d7338674cf50b1","last_reissued_at":"2026-07-05T09:38:22.046080Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:22.046080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cong Wang, Fei Shen, Hu Ye, Jun Zhang, Wei Yang, Xiao Han","submitted_at":"2023-10-10T05:13:17Z","abstract_excerpt":"Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Progressive Conditional Diffusion Models (PCDMs) that incrementally bridge the gap between person images under the target and source poses through three stages. Specifically, in the first stage, we design a simple prior conditional diffusion "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.06313","kind":"arxiv","version":4},"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/2310.06313/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":"2310.06313","created_at":"2026-07-05T09:38:22.046140+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.06313v4","created_at":"2026-07-05T09:38:22.046140+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.06313","created_at":"2026-07-05T09:38:22.046140+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ZOGMKQMJ4A4","created_at":"2026-07-05T09:38:22.046140+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ZOGMKQMJ4A436GD","created_at":"2026-07-05T09:38:22.046140+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ZOGMKQM","created_at":"2026-07-05T09:38:22.046140+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19676","citing_title":"TeleMorpher: Toward Robust Simultaneous Motion-Location Editing","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23178","citing_title":"Composing People Together: Iterative Pose-Image Generation for Multi-Person Interaction Scenes","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20237","citing_title":"AnimeAdapter: A Modular Adapter for Appearance-Consistent Anime Character Generation","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2601.22160","citing_title":"Screen, Cache, and Match: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17051","citing_title":"Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC","json":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC.json","graph_json":"https://pith.science/api/pith-number/3ZOGMKQMJ4A436GDARSFFUB4XC/graph.json","events_json":"https://pith.science/api/pith-number/3ZOGMKQMJ4A436GDARSFFUB4XC/events.json","paper":"https://pith.science/paper/3ZOGMKQM"},"agent_actions":{"view_html":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC","download_json":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC.json","view_paper":"https://pith.science/paper/3ZOGMKQM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.06313&json=true","fetch_graph":"https://pith.science/api/pith-number/3ZOGMKQMJ4A436GDARSFFUB4XC/graph.json","fetch_events":"https://pith.science/api/pith-number/3ZOGMKQMJ4A436GDARSFFUB4XC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC/action/storage_attestation","attest_author":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC/action/author_attestation","sign_citation":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC/action/citation_signature","submit_replication":"https://pith.science/pith/3ZOGMKQMJ4A436GDARSFFUB4XC/action/replication_record"}},"created_at":"2026-07-05T09:38:22.046140+00:00","updated_at":"2026-07-05T09:38:22.046140+00:00"}