{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:34QOJENDXRKYFIHLOE5F74FSL5","short_pith_number":"pith:34QOJEND","schema_version":"1.0","canonical_sha256":"df20e491a3bc5582a0eb713a5ff0b25f7b4242176739a5906a275c67d4cdb950","source":{"kind":"arxiv","id":"2106.11536","version":1},"attestation_state":"computed","paper":{"title":"Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Congyi Wang, Jianjie Zhang, Jinxiang Chai, Juntao Ye, Liguo Jiang, Miaopeng Li, Xinguo Liu","submitted_at":"2021-06-22T04:26:11Z","abstract_excerpt":"We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime. The key idea of our approach is a novel end-to-end multi-task deep learning framework that uses single images to predict five outputs simultaneously: foreground segmentation mask, 2D joints positions, semantic body partitions, 3D part orientations and uv coordinates (uv map). The multi-task network architecture not only generates more visual cues for reconstruction, but also makes each individual prediction more accurate. The CNN regressor is further c"},"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":"2106.11536","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-22T04:26:11Z","cross_cats_sorted":[],"title_canon_sha256":"9a50a2739d32afe6ba048186f7405065a5a3dab60445f8b76993e182482b2444","abstract_canon_sha256":"9e7976f5ea6e3fe08af09c9b4b74f0555ac8a659fb5699d82ade10a658e81008"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:51:30.488724Z","signature_b64":"4PaISVlx629K3WeYsQWKh6ViYpkKq9OxMSyLuUs6EqMe8KIqS+ZX0VcLtTka36u8HCAEiyb2jmXjwxGF7fMMCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df20e491a3bc5582a0eb713a5ff0b25f7b4242176739a5906a275c67d4cdb950","last_reissued_at":"2026-07-05T02:51:30.488232Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:51:30.488232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Congyi Wang, Jianjie Zhang, Jinxiang Chai, Juntao Ye, Liguo Jiang, Miaopeng Li, Xinguo Liu","submitted_at":"2021-06-22T04:26:11Z","abstract_excerpt":"We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime. The key idea of our approach is a novel end-to-end multi-task deep learning framework that uses single images to predict five outputs simultaneously: foreground segmentation mask, 2D joints positions, semantic body partitions, 3D part orientations and uv coordinates (uv map). The multi-task network architecture not only generates more visual cues for reconstruction, but also makes each individual prediction more accurate. The CNN regressor is further c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.11536","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/2106.11536/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":"2106.11536","created_at":"2026-07-05T02:51:30.488289+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.11536v1","created_at":"2026-07-05T02:51:30.488289+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.11536","created_at":"2026-07-05T02:51:30.488289+00:00"},{"alias_kind":"pith_short_12","alias_value":"34QOJENDXRKY","created_at":"2026-07-05T02:51:30.488289+00:00"},{"alias_kind":"pith_short_16","alias_value":"34QOJENDXRKYFIHL","created_at":"2026-07-05T02:51:30.488289+00:00"},{"alias_kind":"pith_short_8","alias_value":"34QOJEND","created_at":"2026-07-05T02:51:30.488289+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/34QOJENDXRKYFIHLOE5F74FSL5","json":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5.json","graph_json":"https://pith.science/api/pith-number/34QOJENDXRKYFIHLOE5F74FSL5/graph.json","events_json":"https://pith.science/api/pith-number/34QOJENDXRKYFIHLOE5F74FSL5/events.json","paper":"https://pith.science/paper/34QOJEND"},"agent_actions":{"view_html":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5","download_json":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5.json","view_paper":"https://pith.science/paper/34QOJEND","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.11536&json=true","fetch_graph":"https://pith.science/api/pith-number/34QOJENDXRKYFIHLOE5F74FSL5/graph.json","fetch_events":"https://pith.science/api/pith-number/34QOJENDXRKYFIHLOE5F74FSL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5/action/storage_attestation","attest_author":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5/action/author_attestation","sign_citation":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5/action/citation_signature","submit_replication":"https://pith.science/pith/34QOJENDXRKYFIHLOE5F74FSL5/action/replication_record"}},"created_at":"2026-07-05T02:51:30.488289+00:00","updated_at":"2026-07-05T02:51:30.488289+00:00"}