{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2SLV6H2PRPNG4MWCJTRN656I62","short_pith_number":"pith:2SLV6H2P","schema_version":"1.0","canonical_sha256":"d4975f1f4f8bda6e32c24ce2df77c8f6bf4edf3b60733bd34664662c5d677a4d","source":{"kind":"arxiv","id":"2502.02358","version":5},"attestation_state":"computed","paper":{"title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"De Wen Soh, Na Zhao, Zeyu Hu, Ziyan Guo","submitted_at":"2025-02-04T14:43:26Z","abstract_excerpt":"Human motion generation and editing are key components of computer vision. However, current approaches in this field tend to offer isolated solutions tailored to specific tasks, which can be inefficient and impractical for real-world applications. While some efforts have aimed to unify motion-related tasks, these methods simply use different modalities as conditions to guide motion generation. Consequently, they lack editing capabilities, fine-grained control, and fail to facilitate knowledge sharing across tasks. To address these limitations and provide a versatile, unified framework capable "},"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":"2502.02358","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-04T14:43:26Z","cross_cats_sorted":[],"title_canon_sha256":"439c65f365e9c1de2f03c76aeff78bdd4327ceb82edd39dd221de464c2a169d8","abstract_canon_sha256":"f8c39da06280db6acc5865592e0200f42ec84cbe958e9b7314efbe85cc1eac7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:52.089007Z","signature_b64":"7bFZp14UzAr+Vwn8UyybvtlUIwxRolw4yjeumNJA/Mi7OZcJLOi4WtuLht7BwOAB/cDEdegUm/rjNJgFB5bJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4975f1f4f8bda6e32c24ce2df77c8f6bf4edf3b60733bd34664662c5d677a4d","last_reissued_at":"2026-07-05T11:40:52.086501Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:52.086501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"De Wen Soh, Na Zhao, Zeyu Hu, Ziyan Guo","submitted_at":"2025-02-04T14:43:26Z","abstract_excerpt":"Human motion generation and editing are key components of computer vision. However, current approaches in this field tend to offer isolated solutions tailored to specific tasks, which can be inefficient and impractical for real-world applications. While some efforts have aimed to unify motion-related tasks, these methods simply use different modalities as conditions to guide motion generation. Consequently, they lack editing capabilities, fine-grained control, and fail to facilitate knowledge sharing across tasks. To address these limitations and provide a versatile, unified framework capable "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02358","kind":"arxiv","version":5},"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/2502.02358/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":"2502.02358","created_at":"2026-07-05T11:40:52.086563+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.02358v5","created_at":"2026-07-05T11:40:52.086563+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02358","created_at":"2026-07-05T11:40:52.086563+00:00"},{"alias_kind":"pith_short_12","alias_value":"2SLV6H2PRPNG","created_at":"2026-07-05T11:40:52.086563+00:00"},{"alias_kind":"pith_short_16","alias_value":"2SLV6H2PRPNG4MWC","created_at":"2026-07-05T11:40:52.086563+00:00"},{"alias_kind":"pith_short_8","alias_value":"2SLV6H2P","created_at":"2026-07-05T11:40:52.086563+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.05419","citing_title":"Motion Generation: A Survey of Generative Approaches and Benchmarks","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62","json":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62.json","graph_json":"https://pith.science/api/pith-number/2SLV6H2PRPNG4MWCJTRN656I62/graph.json","events_json":"https://pith.science/api/pith-number/2SLV6H2PRPNG4MWCJTRN656I62/events.json","paper":"https://pith.science/paper/2SLV6H2P"},"agent_actions":{"view_html":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62","download_json":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62.json","view_paper":"https://pith.science/paper/2SLV6H2P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.02358&json=true","fetch_graph":"https://pith.science/api/pith-number/2SLV6H2PRPNG4MWCJTRN656I62/graph.json","fetch_events":"https://pith.science/api/pith-number/2SLV6H2PRPNG4MWCJTRN656I62/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62/action/storage_attestation","attest_author":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62/action/author_attestation","sign_citation":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62/action/citation_signature","submit_replication":"https://pith.science/pith/2SLV6H2PRPNG4MWCJTRN656I62/action/replication_record"}},"created_at":"2026-07-05T11:40:52.086563+00:00","updated_at":"2026-07-05T11:40:52.086563+00:00"}