{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZWPTDB2ATROVU6Y6HAONAT3O3Z","short_pith_number":"pith:ZWPTDB2A","schema_version":"1.0","canonical_sha256":"cd9f3187409c5d5a7b1e381cd04f6ede7bacfccfca592b68d5782bc2cc77ea6d","source":{"kind":"arxiv","id":"2307.00818","version":2},"attestation_state":"computed","paper":{"title":"Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ailing Zeng, Haoqian Wang, Jing Lin, Lei Zhang, Ruimao Zhang, Shunlin Lu, Yuanhao Cai","submitted_at":"2023-07-03T07:57:29Z","abstract_excerpt":"In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset. Existing motion datasets predominantly contain body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions. Moreover, they are primarily collected from limited laboratory scenes with textual descriptions manually labeled, which greatly limits their scalability. To overcome these limitations, we develop a whole-body motion and text annotation pipeline, which can automatically annotate motion from either single- or multi-view videos and provide comprehensive semantic labe"},"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":"2307.00818","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-03T07:57:29Z","cross_cats_sorted":[],"title_canon_sha256":"0a7ea52354134081cb366d1e8d4d963872eb9a596434219aa789def924934f68","abstract_canon_sha256":"a27f6149947441f6a80d480a3ccefa13e9e002efe5317704bb3d8644c073e459"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:37:50.244386Z","signature_b64":"EdWDN30KMlus/rLm8vhUOcxkPSaNbWAgWTM2aobq1Z+ZTuLnI5Zns8JgZJErj2ceaQhGf1lWddp9HL6Lxww2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd9f3187409c5d5a7b1e381cd04f6ede7bacfccfca592b68d5782bc2cc77ea6d","last_reissued_at":"2026-07-05T07:37:50.243936Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:37:50.243936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ailing Zeng, Haoqian Wang, Jing Lin, Lei Zhang, Ruimao Zhang, Shunlin Lu, Yuanhao Cai","submitted_at":"2023-07-03T07:57:29Z","abstract_excerpt":"In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset. Existing motion datasets predominantly contain body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions. Moreover, they are primarily collected from limited laboratory scenes with textual descriptions manually labeled, which greatly limits their scalability. To overcome these limitations, we develop a whole-body motion and text annotation pipeline, which can automatically annotate motion from either single- or multi-view videos and provide comprehensive semantic labe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.00818","kind":"arxiv","version":2},"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/2307.00818/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":"2307.00818","created_at":"2026-07-05T07:37:50.243994+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.00818v2","created_at":"2026-07-05T07:37:50.243994+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.00818","created_at":"2026-07-05T07:37:50.243994+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZWPTDB2ATROV","created_at":"2026-07-05T07:37:50.243994+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZWPTDB2ATROVU6Y6","created_at":"2026-07-05T07:37:50.243994+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZWPTDB2A","created_at":"2026-07-05T07:37:50.243994+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03476","citing_title":"Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z","json":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z.json","graph_json":"https://pith.science/api/pith-number/ZWPTDB2ATROVU6Y6HAONAT3O3Z/graph.json","events_json":"https://pith.science/api/pith-number/ZWPTDB2ATROVU6Y6HAONAT3O3Z/events.json","paper":"https://pith.science/paper/ZWPTDB2A"},"agent_actions":{"view_html":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z","download_json":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z.json","view_paper":"https://pith.science/paper/ZWPTDB2A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.00818&json=true","fetch_graph":"https://pith.science/api/pith-number/ZWPTDB2ATROVU6Y6HAONAT3O3Z/graph.json","fetch_events":"https://pith.science/api/pith-number/ZWPTDB2ATROVU6Y6HAONAT3O3Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z/action/storage_attestation","attest_author":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z/action/author_attestation","sign_citation":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z/action/citation_signature","submit_replication":"https://pith.science/pith/ZWPTDB2ATROVU6Y6HAONAT3O3Z/action/replication_record"}},"created_at":"2026-07-05T07:37:50.243994+00:00","updated_at":"2026-07-05T07:37:50.243994+00:00"}