{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MYTIYORBHB5MJPLMBCKKVFXH5G","short_pith_number":"pith:MYTIYORB","schema_version":"1.0","canonical_sha256":"66268c3a21387ac4bd6c0894aa96e7e98af4bcdb51c65add5f7757183b69f701","source":{"kind":"arxiv","id":"2503.19557","version":3},"attestation_state":"computed","paper":{"title":"Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amit H. Bermano, Bing Zhou, Chuan Guo, Guy Tevet, Haim Sawdayee, Jian Wang","submitted_at":"2025-03-25T11:23:34Z","abstract_excerpt":"Text-to-motion generative models span a wide range of 3D human actions but struggle with nuanced stylistic attributes such as a \"Chicken\" style. Due to the scarcity of style-specific data, existing approaches pull the generative prior towards a reference style, which often results in out-of-distribution low quality generations. In this work, we introduce LoRA-MDM, a lightweight framework for motion stylization that generalizes to complex actions while maintaining editability. Our key insight is that adapting the generative prior to include the style, while preserving its overall distribution, "},"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":"2503.19557","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-25T11:23:34Z","cross_cats_sorted":[],"title_canon_sha256":"f1e9dfa71ca882bf1d7090b25ee31e504df14da51b0e8e6548d908a7c1c8fb34","abstract_canon_sha256":"3dddbd82c013e568b0ffd2963cf849d82c33f75d81f2595567774bb0ab49393b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:51.796225Z","signature_b64":"ZjAz2LJNX+uhErwM0+Mw11GRI/kXmsyf2F0B24VS2CV5zbqapOZH4G0vprg9BDf6z7jHwSqcbBFfu4phFGWGCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66268c3a21387ac4bd6c0894aa96e7e98af4bcdb51c65add5f7757183b69f701","last_reissued_at":"2026-07-05T11:39:51.795663Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:51.795663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amit H. Bermano, Bing Zhou, Chuan Guo, Guy Tevet, Haim Sawdayee, Jian Wang","submitted_at":"2025-03-25T11:23:34Z","abstract_excerpt":"Text-to-motion generative models span a wide range of 3D human actions but struggle with nuanced stylistic attributes such as a \"Chicken\" style. Due to the scarcity of style-specific data, existing approaches pull the generative prior towards a reference style, which often results in out-of-distribution low quality generations. In this work, we introduce LoRA-MDM, a lightweight framework for motion stylization that generalizes to complex actions while maintaining editability. Our key insight is that adapting the generative prior to include the style, while preserving its overall distribution, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.19557","kind":"arxiv","version":3},"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/2503.19557/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":"2503.19557","created_at":"2026-07-05T11:39:51.795726+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.19557v3","created_at":"2026-07-05T11:39:51.795726+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.19557","created_at":"2026-07-05T11:39:51.795726+00:00"},{"alias_kind":"pith_short_12","alias_value":"MYTIYORBHB5M","created_at":"2026-07-05T11:39:51.795726+00:00"},{"alias_kind":"pith_short_16","alias_value":"MYTIYORBHB5MJPLM","created_at":"2026-07-05T11:39:51.795726+00:00"},{"alias_kind":"pith_short_8","alias_value":"MYTIYORB","created_at":"2026-07-05T11:39:51.795726+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05938","citing_title":"Prior-First, Condition-Second: Scalable and Controllable Hand Motion Completion","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2605.24566","citing_title":"EMA: Effort Metric Attention for Anatomical Effort-Guided Human Motion Diffusion","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25164","citing_title":"IAM: Identity-Aware Human Motion and Shape Joint Generation","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G","json":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G.json","graph_json":"https://pith.science/api/pith-number/MYTIYORBHB5MJPLMBCKKVFXH5G/graph.json","events_json":"https://pith.science/api/pith-number/MYTIYORBHB5MJPLMBCKKVFXH5G/events.json","paper":"https://pith.science/paper/MYTIYORB"},"agent_actions":{"view_html":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G","download_json":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G.json","view_paper":"https://pith.science/paper/MYTIYORB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.19557&json=true","fetch_graph":"https://pith.science/api/pith-number/MYTIYORBHB5MJPLMBCKKVFXH5G/graph.json","fetch_events":"https://pith.science/api/pith-number/MYTIYORBHB5MJPLMBCKKVFXH5G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G/action/storage_attestation","attest_author":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G/action/author_attestation","sign_citation":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G/action/citation_signature","submit_replication":"https://pith.science/pith/MYTIYORBHB5MJPLMBCKKVFXH5G/action/replication_record"}},"created_at":"2026-07-05T11:39:51.795726+00:00","updated_at":"2026-07-05T11:39:51.795726+00:00"}