{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FJDQPOO2QBVGQSHOZ2T35RCZSC","short_pith_number":"pith:FJDQPOO2","schema_version":"1.0","canonical_sha256":"2a4707b9da806a6848eecea7bec4599091534743e6f743a68bc684203f1028f2","source":{"kind":"arxiv","id":"2311.13781","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fanyang Meng, Mengyuan Liu, Shen Zhao, Songtao Wu, Wanying Zhang","submitted_at":"2023-11-23T02:49:46Z","abstract_excerpt":"With potential applications in fields including intelligent surveillance and human-robot interaction, the human motion prediction task has become a hot research topic and also has achieved high success, especially using the recent Graph Convolutional Network (GCN). Current human motion prediction task usually focuses on predicting human motions for atomic actions. Observing that atomic actions can happen at the same time and thus formulating the composite actions, we propose the composite human motion prediction task. To handle this task, we first present a Composite Action Generation (CAG) mo"},"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":"2311.13781","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-23T02:49:46Z","cross_cats_sorted":[],"title_canon_sha256":"ffd8a664429ee98dac051954b9421c407a62f6c2c5d8c1a4b50f36eb2716e5ec","abstract_canon_sha256":"1766bee92516f4361750a3816b70b096d55d3af691d102218aae3c90a12c4fe8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:16:07.948637Z","signature_b64":"7fJy/Vvfo2kwICrAEm5U38r2Q10FZcMmHpkxM3S/pWn/4Cm3KuyC0Rt8pWTitPPAP1wQEv1tXwQVgTvj+fowCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a4707b9da806a6848eecea7bec4599091534743e6f743a68bc684203f1028f2","last_reissued_at":"2026-07-05T07:16:07.948129Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:16:07.948129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fanyang Meng, Mengyuan Liu, Shen Zhao, Songtao Wu, Wanying Zhang","submitted_at":"2023-11-23T02:49:46Z","abstract_excerpt":"With potential applications in fields including intelligent surveillance and human-robot interaction, the human motion prediction task has become a hot research topic and also has achieved high success, especially using the recent Graph Convolutional Network (GCN). Current human motion prediction task usually focuses on predicting human motions for atomic actions. Observing that atomic actions can happen at the same time and thus formulating the composite actions, we propose the composite human motion prediction task. To handle this task, we first present a Composite Action Generation (CAG) mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.13781","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/2311.13781/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":"2311.13781","created_at":"2026-07-05T07:16:07.948175+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.13781v1","created_at":"2026-07-05T07:16:07.948175+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.13781","created_at":"2026-07-05T07:16:07.948175+00:00"},{"alias_kind":"pith_short_12","alias_value":"FJDQPOO2QBVG","created_at":"2026-07-05T07:16:07.948175+00:00"},{"alias_kind":"pith_short_16","alias_value":"FJDQPOO2QBVGQSHO","created_at":"2026-07-05T07:16:07.948175+00:00"},{"alias_kind":"pith_short_8","alias_value":"FJDQPOO2","created_at":"2026-07-05T07:16:07.948175+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/FJDQPOO2QBVGQSHOZ2T35RCZSC","json":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC.json","graph_json":"https://pith.science/api/pith-number/FJDQPOO2QBVGQSHOZ2T35RCZSC/graph.json","events_json":"https://pith.science/api/pith-number/FJDQPOO2QBVGQSHOZ2T35RCZSC/events.json","paper":"https://pith.science/paper/FJDQPOO2"},"agent_actions":{"view_html":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC","download_json":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC.json","view_paper":"https://pith.science/paper/FJDQPOO2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.13781&json=true","fetch_graph":"https://pith.science/api/pith-number/FJDQPOO2QBVGQSHOZ2T35RCZSC/graph.json","fetch_events":"https://pith.science/api/pith-number/FJDQPOO2QBVGQSHOZ2T35RCZSC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC/action/storage_attestation","attest_author":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC/action/author_attestation","sign_citation":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC/action/citation_signature","submit_replication":"https://pith.science/pith/FJDQPOO2QBVGQSHOZ2T35RCZSC/action/replication_record"}},"created_at":"2026-07-05T07:16:07.948175+00:00","updated_at":"2026-07-05T07:16:07.948175+00:00"}