{"id":"W4225375697","doi":"10.24963/ijcai.2022/691","title":"Music-to-Dance Generation with Optimal Transport","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Music and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dance; Choreography; Computer science; Electronic dance music; Rhythm; Artificial intelligence; Visual arts; Aesthetics; Art","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005252816,0.00074918,0.0005245986,0.0003543386,0.000299031,0.0004598013,0.001107293,0.0008996037,0.003731077],"category_scores_gemma":[0.001654382,0.0003105933,0.0005953841,0.000276377,0.0006122313,0.0007367111,0.001126972,0.00109163,0.0006753899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000729916,"about_ca_system_score_gemma":0.0005443469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00299922,"about_ca_topic_score_gemma":0.004399871,"domain_scores_codex":[0.9998026,0.00005045983,0.000009297581,0.00006290538,0.00004537266,0.00002930021],"domain_scores_gemma":[0.999678,0.0001721151,0.00002677006,0.00004564819,0.00004576998,0.00003161361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001624234,0.00005551131,0.0007127526,0.0000665876,0.00002755912,0.0001310261,0.00006648107,0.8896126,0.004558346,0.007238221,0.00265746,0.09471101],"study_design_scores_gemma":[0.000008106405,0.00002751097,0.00006599225,0.000004287337,0.000003214787,0.00002391437,0.000007805919,0.9957381,0.0009621042,0.002684816,0.0004711758,0.000002989321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06097936,0.0004955136,0.928574,0.0004581834,0.000130805,0.0001003595,0.0002628352,0.001722535,0.007276445],"genre_scores_gemma":[0.8528879,0.0002375732,0.1329794,0.0003578949,0.0000521506,0.0001764936,0.0009155404,0.0003291364,0.01206378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003731077,"threshold_uncertainty_score":0.01248169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08363649097721516,"score_gpt":0.2648976207822843,"score_spread":0.1812611298050691,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}