{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:SOXJGSVZMDT3B3ER5RTJT6MWMJ","short_pith_number":"pith:SOXJGSVZ","schema_version":"1.0","canonical_sha256":"93ae934ab960e7b0ec91ec6699f9966252cccadb334a57760a55681f6633880a","source":{"kind":"arxiv","id":"2103.16385","version":1},"attestation_state":"computed","paper":{"title":"Graph Stacked Hourglass Networks for 3D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Tianhan Xu, Wataru Takano","submitted_at":"2021-03-30T14:25:43Z","abstract_excerpt":"In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the pe"},"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":"2103.16385","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T14:25:43Z","cross_cats_sorted":[],"title_canon_sha256":"e1d9ec9e88ead3609445c944cb0beac7f5aefa9637241266146a038bae79f7e6","abstract_canon_sha256":"928ddeebbd893c57b1fa06379d400bc65134576e19a158e0eeaf715ff453f31c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:27:51.349630Z","signature_b64":"JWgncFY95FeF26bBA3r9uu1BkccGCGe2yYpiabGYHKl6efibGkKO/GD5RFaNYTxunfLwSUzGXteAqmDgq29zBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93ae934ab960e7b0ec91ec6699f9966252cccadb334a57760a55681f6633880a","last_reissued_at":"2026-07-05T02:27:51.349171Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:27:51.349171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Stacked Hourglass Networks for 3D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Tianhan Xu, Wataru Takano","submitted_at":"2021-03-30T14:25:43Z","abstract_excerpt":"In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the pe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.16385","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/2103.16385/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":"2103.16385","created_at":"2026-07-05T02:27:51.349231+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.16385v1","created_at":"2026-07-05T02:27:51.349231+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.16385","created_at":"2026-07-05T02:27:51.349231+00:00"},{"alias_kind":"pith_short_12","alias_value":"SOXJGSVZMDT3","created_at":"2026-07-05T02:27:51.349231+00:00"},{"alias_kind":"pith_short_16","alias_value":"SOXJGSVZMDT3B3ER","created_at":"2026-07-05T02:27:51.349231+00:00"},{"alias_kind":"pith_short_8","alias_value":"SOXJGSVZ","created_at":"2026-07-05T02:27:51.349231+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/SOXJGSVZMDT3B3ER5RTJT6MWMJ","json":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ.json","graph_json":"https://pith.science/api/pith-number/SOXJGSVZMDT3B3ER5RTJT6MWMJ/graph.json","events_json":"https://pith.science/api/pith-number/SOXJGSVZMDT3B3ER5RTJT6MWMJ/events.json","paper":"https://pith.science/paper/SOXJGSVZ"},"agent_actions":{"view_html":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ","download_json":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ.json","view_paper":"https://pith.science/paper/SOXJGSVZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.16385&json=true","fetch_graph":"https://pith.science/api/pith-number/SOXJGSVZMDT3B3ER5RTJT6MWMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/SOXJGSVZMDT3B3ER5RTJT6MWMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ/action/storage_attestation","attest_author":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ/action/author_attestation","sign_citation":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ/action/citation_signature","submit_replication":"https://pith.science/pith/SOXJGSVZMDT3B3ER5RTJT6MWMJ/action/replication_record"}},"created_at":"2026-07-05T02:27:51.349231+00:00","updated_at":"2026-07-05T02:27:51.349231+00:00"}