{"id":"W3147968035","doi":"10.1145/3450626.3459670","title":"AMP","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":345,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Motion (physics); Leverage (statistics); Motion capture; Animation; Reinforcement learning; Character animation; Task (project management); Fidelity; Trajectory; Computer vision; Adversarial system; Match moving; Machine learning; Computer animation; Computer graphics (images)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007457031,0.001773571,0.0009811345,0.001784972,0.001580651,0.00391753,0.002524786,0.002665017,0.5335903],"category_scores_gemma":[0.003032346,0.0006908887,0.0009840783,0.001293853,0.0006251512,0.003641403,0.004415814,0.001601627,0.4561548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008614936,"about_ca_system_score_gemma":0.001457382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00204867,"about_ca_topic_score_gemma":0.002180332,"domain_scores_codex":[0.9990219,0.00009948782,0.0000731794,0.0002784774,0.00038826,0.0001387439],"domain_scores_gemma":[0.9986947,0.0001633833,0.0000625203,0.0004235535,0.0004702947,0.0001855977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005269236,0.0001697994,0.00107598,0.0009575711,0.00004508068,0.0004449995,0.0002839627,0.003347818,0.0105585,0.02530801,0.5247646,0.4325169],"study_design_scores_gemma":[0.00005025019,0.0000648355,0.0005726754,0.0001394854,0.00001838105,0.0003042219,0.00009180676,0.004207191,0.003932504,0.007804613,0.9827747,0.00003932933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.004915653,0.001805751,0.1052749,0.00239301,0.003588467,0.0006844539,0.02074891,0.06035707,0.8002319],"genre_scores_gemma":[0.06586555,0.002610802,0.0613378,0.00346059,0.0008356344,0.001001245,0.04854985,0.01169861,0.8046399],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4664097,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02660877344380679,"score_gpt":0.238260135768948,"score_spread":0.2116513623251412,"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."}}