{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AL2YWW2O5M27BVQIQQCLDCMUNJ","short_pith_number":"pith:AL2YWW2O","schema_version":"1.0","canonical_sha256":"02f58b5b4eeb35f0d6088404b189946a68a0ee23229a8ec21937fb3700ee5455","source":{"kind":"arxiv","id":"2308.10305","version":1},"attestation_state":"computed","paper":{"title":"Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Hong Liu, Runwei Ding, Ti Wang, Wenhao Li, Xia Li, Yingxuan You","submitted_at":"2023-08-20T16:03:21Z","abstract_excerpt":"Despite significant progress in single image-based 3D human mesh recovery, accurately and smoothly recovering 3D human motion from a video remains challenging. Existing video-based methods generally recover human mesh by estimating the complex pose and shape parameters from coupled image features, whose high complexity and low representation ability often result in inconsistent pose motion and limited shape patterns. To alleviate this issue, we introduce 3D pose as the intermediary and propose a Pose and Mesh Co-Evolution network (PMCE) that decouples this task into two parts: 1) video-based 3"},"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":"2308.10305","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-20T16:03:21Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"102824e5c3ee7056454464fbdbe076f87ad43d9f16ea7b3ddca777999e176a7f","abstract_canon_sha256":"b68098c9b5b556f5b002c37d07cb3e94f3a7ac177e715b7107a5bd345df4363d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:43:07.452477Z","signature_b64":"jzIAXuGJNsa4Xlx6d4kpMvQuPPXVzDntc/3B7nRnjuZ/MRdHQFfDG6asiTASmHbM1PbK3dBv/JYx//8FAT5GAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02f58b5b4eeb35f0d6088404b189946a68a0ee23229a8ec21937fb3700ee5455","last_reissued_at":"2026-07-05T06:43:07.452146Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:43:07.452146Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Hong Liu, Runwei Ding, Ti Wang, Wenhao Li, Xia Li, Yingxuan You","submitted_at":"2023-08-20T16:03:21Z","abstract_excerpt":"Despite significant progress in single image-based 3D human mesh recovery, accurately and smoothly recovering 3D human motion from a video remains challenging. Existing video-based methods generally recover human mesh by estimating the complex pose and shape parameters from coupled image features, whose high complexity and low representation ability often result in inconsistent pose motion and limited shape patterns. To alleviate this issue, we introduce 3D pose as the intermediary and propose a Pose and Mesh Co-Evolution network (PMCE) that decouples this task into two parts: 1) video-based 3"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10305","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/2308.10305/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":"2308.10305","created_at":"2026-07-05T06:43:07.452193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.10305v1","created_at":"2026-07-05T06:43:07.452193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10305","created_at":"2026-07-05T06:43:07.452193+00:00"},{"alias_kind":"pith_short_12","alias_value":"AL2YWW2O5M27","created_at":"2026-07-05T06:43:07.452193+00:00"},{"alias_kind":"pith_short_16","alias_value":"AL2YWW2O5M27BVQI","created_at":"2026-07-05T06:43:07.452193+00:00"},{"alias_kind":"pith_short_8","alias_value":"AL2YWW2O","created_at":"2026-07-05T06:43:07.452193+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/AL2YWW2O5M27BVQIQQCLDCMUNJ","json":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ.json","graph_json":"https://pith.science/api/pith-number/AL2YWW2O5M27BVQIQQCLDCMUNJ/graph.json","events_json":"https://pith.science/api/pith-number/AL2YWW2O5M27BVQIQQCLDCMUNJ/events.json","paper":"https://pith.science/paper/AL2YWW2O"},"agent_actions":{"view_html":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ","download_json":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ.json","view_paper":"https://pith.science/paper/AL2YWW2O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.10305&json=true","fetch_graph":"https://pith.science/api/pith-number/AL2YWW2O5M27BVQIQQCLDCMUNJ/graph.json","fetch_events":"https://pith.science/api/pith-number/AL2YWW2O5M27BVQIQQCLDCMUNJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ/action/storage_attestation","attest_author":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ/action/author_attestation","sign_citation":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ/action/citation_signature","submit_replication":"https://pith.science/pith/AL2YWW2O5M27BVQIQQCLDCMUNJ/action/replication_record"}},"created_at":"2026-07-05T06:43:07.452193+00:00","updated_at":"2026-07-05T06:43:07.452193+00:00"}