{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LF534FERKANO35OFGZH42CRRPO","short_pith_number":"pith:LF534FER","schema_version":"1.0","canonical_sha256":"597bbe1491501aedf5c5364fcd0a317b84b7da184018d01103ded7e27f4b8e19","source":{"kind":"arxiv","id":"2103.10206","version":5},"attestation_state":"computed","paper":{"title":"DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Buyu Li, Lu Sheng, Yongchi Zhao, Zhelun Shi","submitted_at":"2021-03-18T12:17:38Z","abstract_excerpt":"Generating 3D dances from music is an emerged research task that benefits a lot of applications in vision and graphics. Previous works treat this task as sequence generation, however, it is challenging to render a music-aligned long-term sequence with high kinematic complexity and coherent movements. In this paper, we reformulate it by a two-stage process, ie, a key pose generation and then an in-between parametric motion curve prediction, where the key poses are easier to be synchronized with the music beats and the parametric curves can be efficiently regressed to render fluent rhythm-aligne"},"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":"2103.10206","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-03-18T12:17:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"209bee3931baa827868cdc4d0896117ad901c955fd1f014f13ded5e9e7fb20e8","abstract_canon_sha256":"af9e92af25c7d7cb6770844ee2075d2816312817ef02af66e0519750ad503c30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:35:02.842354Z","signature_b64":"Hh85ssBpAASbFXxN9Xus4o33V26BNP4zcpaTAyllu9wcm4FqmUeMsPA+16Ew7wT89J7zxL2IB0kCxCaNytkTCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"597bbe1491501aedf5c5364fcd0a317b84b7da184018d01103ded7e27f4b8e19","last_reissued_at":"2026-07-05T06:35:02.841918Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:35:02.841918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Buyu Li, Lu Sheng, Yongchi Zhao, Zhelun Shi","submitted_at":"2021-03-18T12:17:38Z","abstract_excerpt":"Generating 3D dances from music is an emerged research task that benefits a lot of applications in vision and graphics. Previous works treat this task as sequence generation, however, it is challenging to render a music-aligned long-term sequence with high kinematic complexity and coherent movements. In this paper, we reformulate it by a two-stage process, ie, a key pose generation and then an in-between parametric motion curve prediction, where the key poses are easier to be synchronized with the music beats and the parametric curves can be efficiently regressed to render fluent rhythm-aligne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.10206","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/2103.10206/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":"2103.10206","created_at":"2026-07-05T06:35:02.841982+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.10206v5","created_at":"2026-07-05T06:35:02.841982+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.10206","created_at":"2026-07-05T06:35:02.841982+00:00"},{"alias_kind":"pith_short_12","alias_value":"LF534FERKANO","created_at":"2026-07-05T06:35:02.841982+00:00"},{"alias_kind":"pith_short_16","alias_value":"LF534FERKANO35OF","created_at":"2026-07-05T06:35:02.841982+00:00"},{"alias_kind":"pith_short_8","alias_value":"LF534FER","created_at":"2026-07-05T06:35:02.841982+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/LF534FERKANO35OFGZH42CRRPO","json":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO.json","graph_json":"https://pith.science/api/pith-number/LF534FERKANO35OFGZH42CRRPO/graph.json","events_json":"https://pith.science/api/pith-number/LF534FERKANO35OFGZH42CRRPO/events.json","paper":"https://pith.science/paper/LF534FER"},"agent_actions":{"view_html":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO","download_json":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO.json","view_paper":"https://pith.science/paper/LF534FER","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.10206&json=true","fetch_graph":"https://pith.science/api/pith-number/LF534FERKANO35OFGZH42CRRPO/graph.json","fetch_events":"https://pith.science/api/pith-number/LF534FERKANO35OFGZH42CRRPO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO/action/storage_attestation","attest_author":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO/action/author_attestation","sign_citation":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO/action/citation_signature","submit_replication":"https://pith.science/pith/LF534FERKANO35OFGZH42CRRPO/action/replication_record"}},"created_at":"2026-07-05T06:35:02.841982+00:00","updated_at":"2026-07-05T06:35:02.841982+00:00"}