{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CERSQ2TR7QEIOKXKUN2ZRR2Z2P","short_pith_number":"pith:CERSQ2TR","schema_version":"1.0","canonical_sha256":"1123286a71fc08872aeaa37598c759d3c3bf87f46bea91a4f74a171dc00b8410","source":{"kind":"arxiv","id":"2404.00054","version":1},"attestation_state":"computed","paper":{"title":"Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.HC","authors_text":"Cornelia Ferm\\\"uller, Kate Ladenheim, Siyuan Peng, Snehesh Shrestha","submitted_at":"2024-03-26T01:42:13Z","abstract_excerpt":"This paper introduces the concept of a design tool for artistic performances based on attribute descriptions. To do so, we used a specific performance of falling actions. The platform integrates a novel machine-learning (ML) model with an interactive interface to generate and visualize artistic movements. Our approach's core is a cyclic Attribute-Conditioned Variational Autoencoder (AC-VAE) model developed to address the challenge of capturing and generating realistic 3D human body motions from motion capture (MoCap) data. We created a unique dataset focused on the dynamics of falling movement"},"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":"2404.00054","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-03-26T01:42:13Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"ef55a5705eae01188718e666b601f859ced6a2e54256065b5ff4d8959a92c931","abstract_canon_sha256":"a4e928473d244f7cf151cb0f66a564cb04b63b4d4fa1eefba7944fad45786885"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:27.036979Z","signature_b64":"P8FVfs3m95cgMtqw3HB4xPxJG9xmxy5j4Fdwx4iyMb4SOneB0kSjeZFDzkzdaPPDQuu7lTqx42lq3BdMfNN/Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1123286a71fc08872aeaa37598c759d3c3bf87f46bea91a4f74a171dc00b8410","last_reissued_at":"2026-07-05T08:02:27.036510Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:27.036510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.HC","authors_text":"Cornelia Ferm\\\"uller, Kate Ladenheim, Siyuan Peng, Snehesh Shrestha","submitted_at":"2024-03-26T01:42:13Z","abstract_excerpt":"This paper introduces the concept of a design tool for artistic performances based on attribute descriptions. To do so, we used a specific performance of falling actions. The platform integrates a novel machine-learning (ML) model with an interactive interface to generate and visualize artistic movements. Our approach's core is a cyclic Attribute-Conditioned Variational Autoencoder (AC-VAE) model developed to address the challenge of capturing and generating realistic 3D human body motions from motion capture (MoCap) data. We created a unique dataset focused on the dynamics of falling movement"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00054","kind":"arxiv","version":1},"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/2404.00054/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":"2404.00054","created_at":"2026-07-05T08:02:27.036559+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.00054v1","created_at":"2026-07-05T08:02:27.036559+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00054","created_at":"2026-07-05T08:02:27.036559+00:00"},{"alias_kind":"pith_short_12","alias_value":"CERSQ2TR7QEI","created_at":"2026-07-05T08:02:27.036559+00:00"},{"alias_kind":"pith_short_16","alias_value":"CERSQ2TR7QEIOKXK","created_at":"2026-07-05T08:02:27.036559+00:00"},{"alias_kind":"pith_short_8","alias_value":"CERSQ2TR","created_at":"2026-07-05T08:02:27.036559+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22726","citing_title":"Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P","json":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P.json","graph_json":"https://pith.science/api/pith-number/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/graph.json","events_json":"https://pith.science/api/pith-number/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/events.json","paper":"https://pith.science/paper/CERSQ2TR"},"agent_actions":{"view_html":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P","download_json":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P.json","view_paper":"https://pith.science/paper/CERSQ2TR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.00054&json=true","fetch_graph":"https://pith.science/api/pith-number/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/graph.json","fetch_events":"https://pith.science/api/pith-number/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/action/storage_attestation","attest_author":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/action/author_attestation","sign_citation":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/action/citation_signature","submit_replication":"https://pith.science/pith/CERSQ2TR7QEIOKXKUN2ZRR2Z2P/action/replication_record"}},"created_at":"2026-07-05T08:02:27.036559+00:00","updated_at":"2026-07-05T08:02:27.036559+00:00"}