{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:O4YTV5KWMIS2DMY2SB6XWF4JA6","short_pith_number":"pith:O4YTV5KW","schema_version":"1.0","canonical_sha256":"77313af5566225a1b31a907d7b178907a8539cfd46155187a6568751e9f7c432","source":{"kind":"arxiv","id":"2209.09124","version":2},"attestation_state":"computed","paper":{"title":"DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Mo Chen, Mohammad Mahdavian, Payam Nikdel","submitted_at":"2022-09-13T23:22:33Z","abstract_excerpt":"Human motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in human motion prediction, most studies simplify the problem by predicting the human motion relative to a fixed joint and/or only limit their model to predict one possible future motion. While due to the complex nature of human motion, a single output cannot reflect all the possible actions one can do. Also, for any robotics application, we need the full human motion including the user trajectory not a 3d pose relative to the hip joint.\n  In this paper, we try to address these two issue"},"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":"2209.09124","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-09-13T23:22:33Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"b1f0d3f509f380976601a4609b6083fe35c59a138a75712c637c768741d593d6","abstract_canon_sha256":"719774114475feb46be2a6fd6d5afc0ce40981bdc7a561034a53d454af421579"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:02:44.546639Z","signature_b64":"Ozq5oCtU2+R0RrPhLNE5sUaRiEVc5d+Fnnr2IKtLNFX0eAwKra1cEjmBjOuiTAxfoGDDUzLewy+Saw6LGvm/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77313af5566225a1b31a907d7b178907a8539cfd46155187a6568751e9f7c432","last_reissued_at":"2026-07-05T05:02:44.546178Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:02:44.546178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Mo Chen, Mohammad Mahdavian, Payam Nikdel","submitted_at":"2022-09-13T23:22:33Z","abstract_excerpt":"Human motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in human motion prediction, most studies simplify the problem by predicting the human motion relative to a fixed joint and/or only limit their model to predict one possible future motion. While due to the complex nature of human motion, a single output cannot reflect all the possible actions one can do. Also, for any robotics application, we need the full human motion including the user trajectory not a 3d pose relative to the hip joint.\n  In this paper, we try to address these two issue"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.09124","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/2209.09124/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":"2209.09124","created_at":"2026-07-05T05:02:44.546234+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.09124v2","created_at":"2026-07-05T05:02:44.546234+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.09124","created_at":"2026-07-05T05:02:44.546234+00:00"},{"alias_kind":"pith_short_12","alias_value":"O4YTV5KWMIS2","created_at":"2026-07-05T05:02:44.546234+00:00"},{"alias_kind":"pith_short_16","alias_value":"O4YTV5KWMIS2DMY2","created_at":"2026-07-05T05:02:44.546234+00:00"},{"alias_kind":"pith_short_8","alias_value":"O4YTV5KW","created_at":"2026-07-05T05:02:44.546234+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/O4YTV5KWMIS2DMY2SB6XWF4JA6","json":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6.json","graph_json":"https://pith.science/api/pith-number/O4YTV5KWMIS2DMY2SB6XWF4JA6/graph.json","events_json":"https://pith.science/api/pith-number/O4YTV5KWMIS2DMY2SB6XWF4JA6/events.json","paper":"https://pith.science/paper/O4YTV5KW"},"agent_actions":{"view_html":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6","download_json":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6.json","view_paper":"https://pith.science/paper/O4YTV5KW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.09124&json=true","fetch_graph":"https://pith.science/api/pith-number/O4YTV5KWMIS2DMY2SB6XWF4JA6/graph.json","fetch_events":"https://pith.science/api/pith-number/O4YTV5KWMIS2DMY2SB6XWF4JA6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6/action/storage_attestation","attest_author":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6/action/author_attestation","sign_citation":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6/action/citation_signature","submit_replication":"https://pith.science/pith/O4YTV5KWMIS2DMY2SB6XWF4JA6/action/replication_record"}},"created_at":"2026-07-05T05:02:44.546234+00:00","updated_at":"2026-07-05T05:02:44.546234+00:00"}