{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OTENWNCPJDQKLMEKOZZBGZ555J","short_pith_number":"pith:OTENWNCP","schema_version":"1.0","canonical_sha256":"74c8db344f48e0a5b08a76721367bdea5094d521e29e8306ba1ff01cbe60546c","source":{"kind":"arxiv","id":"2506.02853","version":1},"attestation_state":"computed","paper":{"title":"Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangming Shi, Mingjie Wei, Xuemei Xie, Yutong Zhong","submitted_at":"2025-06-03T13:21:37Z","abstract_excerpt":"Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there are two main challenges in modeling long-range dependencies. First, joints should not only be constrained by other individual joints but also be modulated by the body parts. Second, existing methods make networks deeper to learn dependencies between non-linked parts. They introduce uncorrelated noise and increase the model size. In this paper, we utilize a pyramid structure to"},"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":"2506.02853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-03T13:21:37Z","cross_cats_sorted":[],"title_canon_sha256":"15117ae94dc15fed913ed816852a2a24c79b01d4815d7fe7184c7d4d91da75be","abstract_canon_sha256":"1cd1a84837a08cde4ae82ff7744d5dd4e59a3ec7d7421288e3f289d41a041be5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:10.450993Z","signature_b64":"MALZT4GTJrobtQXyUneBEr3rCFMogbEhHiriOptZ9MOUCQ+MCin43POG5R2hXHQsiiA2xxxsckKj081x0b/bDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74c8db344f48e0a5b08a76721367bdea5094d521e29e8306ba1ff01cbe60546c","last_reissued_at":"2026-07-05T11:15:10.450484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:10.450484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangming Shi, Mingjie Wei, Xuemei Xie, Yutong Zhong","submitted_at":"2025-06-03T13:21:37Z","abstract_excerpt":"Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there are two main challenges in modeling long-range dependencies. First, joints should not only be constrained by other individual joints but also be modulated by the body parts. Second, existing methods make networks deeper to learn dependencies between non-linked parts. They introduce uncorrelated noise and increase the model size. In this paper, we utilize a pyramid structure to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02853","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/2506.02853/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":"2506.02853","created_at":"2026-07-05T11:15:10.450549+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.02853v1","created_at":"2026-07-05T11:15:10.450549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02853","created_at":"2026-07-05T11:15:10.450549+00:00"},{"alias_kind":"pith_short_12","alias_value":"OTENWNCPJDQK","created_at":"2026-07-05T11:15:10.450549+00:00"},{"alias_kind":"pith_short_16","alias_value":"OTENWNCPJDQKLMEK","created_at":"2026-07-05T11:15:10.450549+00:00"},{"alias_kind":"pith_short_8","alias_value":"OTENWNCP","created_at":"2026-07-05T11:15:10.450549+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/OTENWNCPJDQKLMEKOZZBGZ555J","json":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J.json","graph_json":"https://pith.science/api/pith-number/OTENWNCPJDQKLMEKOZZBGZ555J/graph.json","events_json":"https://pith.science/api/pith-number/OTENWNCPJDQKLMEKOZZBGZ555J/events.json","paper":"https://pith.science/paper/OTENWNCP"},"agent_actions":{"view_html":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J","download_json":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J.json","view_paper":"https://pith.science/paper/OTENWNCP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.02853&json=true","fetch_graph":"https://pith.science/api/pith-number/OTENWNCPJDQKLMEKOZZBGZ555J/graph.json","fetch_events":"https://pith.science/api/pith-number/OTENWNCPJDQKLMEKOZZBGZ555J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J/action/storage_attestation","attest_author":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J/action/author_attestation","sign_citation":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J/action/citation_signature","submit_replication":"https://pith.science/pith/OTENWNCPJDQKLMEKOZZBGZ555J/action/replication_record"}},"created_at":"2026-07-05T11:15:10.450549+00:00","updated_at":"2026-07-05T11:15:10.450549+00:00"}