{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OAV34FOG25EWPRIEBBOH7ME7TF","short_pith_number":"pith:OAV34FOG","schema_version":"1.0","canonical_sha256":"702bbe15c6d74967c504085c7fb09f994e35616a354744662bb82feed3e00599","source":{"kind":"arxiv","id":"2410.21747","version":2},"attestation_state":"computed","paper":{"title":"MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dan Xu, Di Huang, Jile Jiao, Shixiang Tang, Wanli Ouyang, Xuetao Feng, Yaqi Zhang, Yuan Wang","submitted_at":"2024-10-29T05:25:34Z","abstract_excerpt":"Generating lifelike human motions from descriptive texts has experienced remarkable research focus in the recent years, propelled by the emerging requirements of digital humans.Despite impressive advances, existing approaches are often constrained by limited control modalities, task specificity, and focus solely on body motion representations.In this paper, we present MotionGPT-2, a unified Large Motion-Language Model (LMLM) that addresses these limitations. MotionGPT-2 accommodates multiple motion-relevant tasks and supporting multimodal control conditions through pre-trained Large Language M"},"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":"2410.21747","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-29T05:25:34Z","cross_cats_sorted":[],"title_canon_sha256":"a2c942aa07a5e9e25aad268fcbe4d20d41d7f79161ebcdca53b3686686452b31","abstract_canon_sha256":"9288734e0b979ef670afde9d2e75e865a438f89dbd5cd0fe9b2e71b585c27b0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:00.803780Z","signature_b64":"eG9HPTrpEQwI/YaT7qCxTZhZtnmiFj6Y3JMsEbkAnBuZjJ46IjJRO5+zUIXtR/ExKFy8Npf95/oY9NOSSfF2Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"702bbe15c6d74967c504085c7fb09f994e35616a354744662bb82feed3e00599","last_reissued_at":"2026-06-09T02:07:00.802815Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:00.802815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dan Xu, Di Huang, Jile Jiao, Shixiang Tang, Wanli Ouyang, Xuetao Feng, Yaqi Zhang, Yuan Wang","submitted_at":"2024-10-29T05:25:34Z","abstract_excerpt":"Generating lifelike human motions from descriptive texts has experienced remarkable research focus in the recent years, propelled by the emerging requirements of digital humans.Despite impressive advances, existing approaches are often constrained by limited control modalities, task specificity, and focus solely on body motion representations.In this paper, we present MotionGPT-2, a unified Large Motion-Language Model (LMLM) that addresses these limitations. MotionGPT-2 accommodates multiple motion-relevant tasks and supporting multimodal control conditions through pre-trained Large Language M"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.21747","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/2410.21747/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":"2410.21747","created_at":"2026-06-09T02:07:00.802926+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.21747v2","created_at":"2026-06-09T02:07:00.802926+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.21747","created_at":"2026-06-09T02:07:00.802926+00:00"},{"alias_kind":"pith_short_12","alias_value":"OAV34FOG25EW","created_at":"2026-06-09T02:07:00.802926+00:00"},{"alias_kind":"pith_short_16","alias_value":"OAV34FOG25EWPRIE","created_at":"2026-06-09T02:07:00.802926+00:00"},{"alias_kind":"pith_short_8","alias_value":"OAV34FOG","created_at":"2026-06-09T02:07:00.802926+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":7,"sample":[{"citing_arxiv_id":"2605.22894","citing_title":"SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control","ref_index":52,"is_internal_anchor":true},{"citing_arxiv_id":"2512.14234","citing_title":"ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body","ref_index":112,"is_internal_anchor":true},{"citing_arxiv_id":"2602.12370","citing_title":"LLaMo: Scaling Pretrained Language Models for Unified Motion Understanding and Generation with Continuous Autoregressive Tokens","ref_index":71,"is_internal_anchor":true},{"citing_arxiv_id":"2604.03799","citing_title":"Next-Scale Autoregressive Models for Text-to-Motion Generation","ref_index":51,"is_internal_anchor":true},{"citing_arxiv_id":"2604.21668","citing_title":"Encoder-Free Human Motion Understanding via Structured Motion Descriptions","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10466","citing_title":"ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos","ref_index":55,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10490","citing_title":"Make it Simple, Make it Dance: Dance Motion Simplification to Support Novices' Dance Learning","ref_index":86,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF","json":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF.json","graph_json":"https://pith.science/api/pith-number/OAV34FOG25EWPRIEBBOH7ME7TF/graph.json","events_json":"https://pith.science/api/pith-number/OAV34FOG25EWPRIEBBOH7ME7TF/events.json","paper":"https://pith.science/paper/OAV34FOG"},"agent_actions":{"view_html":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF","download_json":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF.json","view_paper":"https://pith.science/paper/OAV34FOG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.21747&json=true","fetch_graph":"https://pith.science/api/pith-number/OAV34FOG25EWPRIEBBOH7ME7TF/graph.json","fetch_events":"https://pith.science/api/pith-number/OAV34FOG25EWPRIEBBOH7ME7TF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF/action/storage_attestation","attest_author":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF/action/author_attestation","sign_citation":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF/action/citation_signature","submit_replication":"https://pith.science/pith/OAV34FOG25EWPRIEBBOH7ME7TF/action/replication_record"}},"created_at":"2026-06-09T02:07:00.802926+00:00","updated_at":"2026-06-09T02:07:00.802926+00:00"}