{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WWGDW3CFFUXHYAFOW3LN3TA6HT","short_pith_number":"pith:WWGDW3CF","schema_version":"1.0","canonical_sha256":"b58c3b6c452d2e7c00aeb6d6ddcc1e3cf9ff0a46bda57741ff3f9ae8128bf180","source":{"kind":"arxiv","id":"2308.16801","version":1},"attestation_state":"computed","paper":{"title":"Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Etemad, Michael Greenspan, Mohsen Zand","submitted_at":"2023-08-31T15:23:33Z","abstract_excerpt":"A new method is proposed for human motion prediction by learning temporal and spatial dependencies. Recently, multiscale graphs have been developed to model the human body at higher abstraction levels, resulting in more stable motion prediction. Current methods however predetermine scale levels and combine spatially proximal joints to generate coarser scales based on human priors, even though movement patterns in different motion sequences vary and do not fully comply with a fixed graph of spatially connected joints. Another problem with graph convolutional methods is mode collapse, in which p"},"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":"2308.16801","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-31T15:23:33Z","cross_cats_sorted":[],"title_canon_sha256":"09a2dd2a48fb4d7c1bd387abdc55c3ddc10ed1cd2e6745f5fc42522dafa6a4cb","abstract_canon_sha256":"2092f70257c9b90531b3fef5970ba811dd6aa3d43fec68d7c6636ba455d3abcd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:46:39.389713Z","signature_b64":"dnfabZXe9TDdwuZYBCbp9BgYy5XHrx03DycE6YePo2T1DOEFlP8KxPxs8Fz4+ew3wVqJ9imFbEKVkgc8MdsxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b58c3b6c452d2e7c00aeb6d6ddcc1e3cf9ff0a46bda57741ff3f9ae8128bf180","last_reissued_at":"2026-07-05T06:46:39.389197Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:46:39.389197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Etemad, Michael Greenspan, Mohsen Zand","submitted_at":"2023-08-31T15:23:33Z","abstract_excerpt":"A new method is proposed for human motion prediction by learning temporal and spatial dependencies. Recently, multiscale graphs have been developed to model the human body at higher abstraction levels, resulting in more stable motion prediction. Current methods however predetermine scale levels and combine spatially proximal joints to generate coarser scales based on human priors, even though movement patterns in different motion sequences vary and do not fully comply with a fixed graph of spatially connected joints. Another problem with graph convolutional methods is mode collapse, in which p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.16801","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/2308.16801/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":"2308.16801","created_at":"2026-07-05T06:46:39.389274+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.16801v1","created_at":"2026-07-05T06:46:39.389274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.16801","created_at":"2026-07-05T06:46:39.389274+00:00"},{"alias_kind":"pith_short_12","alias_value":"WWGDW3CFFUXH","created_at":"2026-07-05T06:46:39.389274+00:00"},{"alias_kind":"pith_short_16","alias_value":"WWGDW3CFFUXHYAFO","created_at":"2026-07-05T06:46:39.389274+00:00"},{"alias_kind":"pith_short_8","alias_value":"WWGDW3CF","created_at":"2026-07-05T06:46:39.389274+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11632","citing_title":"Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT","json":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT.json","graph_json":"https://pith.science/api/pith-number/WWGDW3CFFUXHYAFOW3LN3TA6HT/graph.json","events_json":"https://pith.science/api/pith-number/WWGDW3CFFUXHYAFOW3LN3TA6HT/events.json","paper":"https://pith.science/paper/WWGDW3CF"},"agent_actions":{"view_html":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT","download_json":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT.json","view_paper":"https://pith.science/paper/WWGDW3CF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.16801&json=true","fetch_graph":"https://pith.science/api/pith-number/WWGDW3CFFUXHYAFOW3LN3TA6HT/graph.json","fetch_events":"https://pith.science/api/pith-number/WWGDW3CFFUXHYAFOW3LN3TA6HT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT/action/storage_attestation","attest_author":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT/action/author_attestation","sign_citation":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT/action/citation_signature","submit_replication":"https://pith.science/pith/WWGDW3CFFUXHYAFOW3LN3TA6HT/action/replication_record"}},"created_at":"2026-07-05T06:46:39.389274+00:00","updated_at":"2026-07-05T06:46:39.389274+00:00"}