{"id":"W3048665613","doi":"10.1145/3386569.3392440","title":"Learned motion matching","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Ubisoft (Canada)","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Animation; Matching (statistics); Artificial neural network; Scalability; Artificial intelligence; Generative model; Flexibility (engineering); Motion (physics); Bottleneck; Preprocessor; Generative grammar; Machine learning; Database; Computer graphics (images)","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":[0.0006299774,0.0007694788,0.0009146193,0.0009127572,0.0004718175,0.00133879,0.002575454,0.001610672,0.01399658],"category_scores_gemma":[0.003790523,0.0004939198,0.0009537325,0.0009370695,0.0008573221,0.002926003,0.002135591,0.001382659,0.002650779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009378694,"about_ca_system_score_gemma":0.001034939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003999074,"about_ca_topic_score_gemma":0.004169615,"domain_scores_codex":[0.9990822,0.00008414976,0.00004811997,0.0003856603,0.0003149658,0.00008489229],"domain_scores_gemma":[0.999329,0.0001657447,0.00006476657,0.0002710037,0.0001238099,0.00004569182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002387853,0.0001676278,0.001015082,0.0001491616,0.00008888224,0.0001334363,0.00009891635,0.3839031,0.01202238,0.0565948,0.005699467,0.5398884],"study_design_scores_gemma":[0.00002337761,0.00005298249,0.0002035966,0.00001224471,0.00001106755,0.00006448316,0.00001706391,0.9719515,0.003797123,0.01854729,0.005306011,0.00001325121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007750294,0.0001165285,0.9873394,0.0001000103,0.00008181895,0.00007182367,0.0001151696,0.001163368,0.003261602],"genre_scores_gemma":[0.3718717,0.0003531827,0.6031591,0.0004746874,0.0001434412,0.0002831336,0.00142545,0.0007546897,0.02153455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01399658,"threshold_uncertainty_score":0.0468232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04042371305013918,"score_gpt":0.2411348440739292,"score_spread":0.20071113102379,"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."}}