{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:V2R6MQQXQYDWSGX3HXRWW7BT7S","short_pith_number":"pith:V2R6MQQX","schema_version":"1.0","canonical_sha256":"aea3e642178607691afb3de36b7c33fca1be621f768f11f8075f304881a5f299","source":{"kind":"arxiv","id":"2403.07487","version":4},"attestation_state":"computed","paper":{"title":"Motion Mamba: Efficient and Long Sequence Motion Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Akide Liu, Bohan Zhuang, Hao Tang, Ian Reid, Richard Hartley, Zeyu Zhang","submitted_at":"2024-03-12T10:25:29Z","abstract_excerpt":"Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a promising direction to build motion generation model upon it. Nevertheless, adapting SSMs to motion generation faces hurdles since the lack of a specialized design architecture to model motion sequence. To address these challenges, we propo"},"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":"2403.07487","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-12T10:25:29Z","cross_cats_sorted":[],"title_canon_sha256":"861121a596263dac3d2b9d3ba5adc97794e71a0bf786489ec1bde7bbfa2bfe8a","abstract_canon_sha256":"744ce786f71649226e0de0d855b6cd2e28814cca04ebee9c234060d307a227a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:49.179329Z","signature_b64":"E6BsEg9j399lqhKtbPBerd69n3CK7lu6zh3/1B7gCTWUKy3xx4/lknqoBhITXRHMU0rU0reoXVrBh8G2abpCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aea3e642178607691afb3de36b7c33fca1be621f768f11f8075f304881a5f299","last_reissued_at":"2026-07-05T08:51:49.178621Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:49.178621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Motion Mamba: Efficient and Long Sequence Motion Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Akide Liu, Bohan Zhuang, Hao Tang, Ian Reid, Richard Hartley, Zeyu Zhang","submitted_at":"2024-03-12T10:25:29Z","abstract_excerpt":"Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a promising direction to build motion generation model upon it. Nevertheless, adapting SSMs to motion generation faces hurdles since the lack of a specialized design architecture to model motion sequence. To address these challenges, we propo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07487","kind":"arxiv","version":4},"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/2403.07487/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":"2403.07487","created_at":"2026-07-05T08:51:49.178679+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.07487v4","created_at":"2026-07-05T08:51:49.178679+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07487","created_at":"2026-07-05T08:51:49.178679+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2R6MQQXQYDW","created_at":"2026-07-05T08:51:49.178679+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2R6MQQXQYDWSGX3","created_at":"2026-07-05T08:51:49.178679+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2R6MQQX","created_at":"2026-07-05T08:51:49.178679+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.01129","citing_title":"A Survey of Mamba","ref_index":237,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S","json":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S.json","graph_json":"https://pith.science/api/pith-number/V2R6MQQXQYDWSGX3HXRWW7BT7S/graph.json","events_json":"https://pith.science/api/pith-number/V2R6MQQXQYDWSGX3HXRWW7BT7S/events.json","paper":"https://pith.science/paper/V2R6MQQX"},"agent_actions":{"view_html":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S","download_json":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S.json","view_paper":"https://pith.science/paper/V2R6MQQX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.07487&json=true","fetch_graph":"https://pith.science/api/pith-number/V2R6MQQXQYDWSGX3HXRWW7BT7S/graph.json","fetch_events":"https://pith.science/api/pith-number/V2R6MQQXQYDWSGX3HXRWW7BT7S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S/action/storage_attestation","attest_author":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S/action/author_attestation","sign_citation":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S/action/citation_signature","submit_replication":"https://pith.science/pith/V2R6MQQXQYDWSGX3HXRWW7BT7S/action/replication_record"}},"created_at":"2026-07-05T08:51:49.178679+00:00","updated_at":"2026-07-05T08:51:49.178679+00:00"}