{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:66PAJ77FTBNRQTZ64ULJWUFDNM","short_pith_number":"pith:66PAJ77F","schema_version":"1.0","canonical_sha256":"f79e04ffe5985b184f3ee5169b50a36b12f57e329464734670714194ffb512d6","source":{"kind":"arxiv","id":"2211.03375","version":1},"attestation_state":"computed","paper":{"title":"AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Chao Xu, Hao-Shu Fang, Haoyi Zhu, Hongyang Tang, Jiefeng Li, Yong-Lu Li, Yuliang Xiu","submitted_at":"2022-11-07T09:15:38Z","abstract_excerpt":"Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric "},"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":"2211.03375","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-07T09:15:38Z","cross_cats_sorted":[],"title_canon_sha256":"256301b15908913006c3486c254f1966d4dc292b1fef25fd5e1ab84da79bfb85","abstract_canon_sha256":"c8779fdccfee0e7c43c7df9ae72fa1ea7a27a4280010d160c19ca09f63b31ae2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:47.954908Z","signature_b64":"gqa61CwjsjaV1stJsts2SNSRD/4VZkm83jLLkJ3TROqlrz7madJK9N/vVwyX2hGrzi8Gz6eOIS+D98w7reV+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f79e04ffe5985b184f3ee5169b50a36b12f57e329464734670714194ffb512d6","last_reissued_at":"2026-07-05T05:13:47.954477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:47.954477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Chao Xu, Hao-Shu Fang, Haoyi Zhu, Hongyang Tang, Jiefeng Li, Yong-Lu Li, Yuliang Xiu","submitted_at":"2022-11-07T09:15:38Z","abstract_excerpt":"Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.03375","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/2211.03375/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":"2211.03375","created_at":"2026-07-05T05:13:47.954535+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.03375v1","created_at":"2026-07-05T05:13:47.954535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.03375","created_at":"2026-07-05T05:13:47.954535+00:00"},{"alias_kind":"pith_short_12","alias_value":"66PAJ77FTBNR","created_at":"2026-07-05T05:13:47.954535+00:00"},{"alias_kind":"pith_short_16","alias_value":"66PAJ77FTBNRQTZ6","created_at":"2026-07-05T05:13:47.954535+00:00"},{"alias_kind":"pith_short_8","alias_value":"66PAJ77F","created_at":"2026-07-05T05:13:47.954535+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.09672","citing_title":"VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM","json":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM.json","graph_json":"https://pith.science/api/pith-number/66PAJ77FTBNRQTZ64ULJWUFDNM/graph.json","events_json":"https://pith.science/api/pith-number/66PAJ77FTBNRQTZ64ULJWUFDNM/events.json","paper":"https://pith.science/paper/66PAJ77F"},"agent_actions":{"view_html":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM","download_json":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM.json","view_paper":"https://pith.science/paper/66PAJ77F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.03375&json=true","fetch_graph":"https://pith.science/api/pith-number/66PAJ77FTBNRQTZ64ULJWUFDNM/graph.json","fetch_events":"https://pith.science/api/pith-number/66PAJ77FTBNRQTZ64ULJWUFDNM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM/action/storage_attestation","attest_author":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM/action/author_attestation","sign_citation":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM/action/citation_signature","submit_replication":"https://pith.science/pith/66PAJ77FTBNRQTZ64ULJWUFDNM/action/replication_record"}},"created_at":"2026-07-05T05:13:47.954535+00:00","updated_at":"2026-07-05T05:13:47.954535+00:00"}