{"id":"W2796290181","doi":"10.1145/3197517.3201311","title":"DeepMimic","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":803,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Animation; Character animation; Motion capture; Generality; Artificial intelligence; Computer animation; Human–computer interaction; Motion (physics); Reinforcement learning; Flexibility (engineering); Robot; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003865705,0.00006832355,0.00004730236,0.0001388999,0.0001193869,0.00001770439,0.00009636336,0.00005175906,0.0004878766],"category_scores_gemma":[0.000004088298,0.00007207844,0.00004553851,0.0002145984,0.00004388938,0.00007821293,5.268674e-7,0.0001210579,0.0004339321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001366117,"about_ca_system_score_gemma":0.000003256888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001430603,"about_ca_topic_score_gemma":0.00004391745,"domain_scores_codex":[0.9996535,0.000007596662,0.00008612496,0.0000743285,0.00007743906,0.0001009943],"domain_scores_gemma":[0.9996927,0.00001731519,0.000006308572,0.0002159662,0.00002799137,0.00003966055],"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":[0.0001067899,0.0009680483,0.0005643814,0.000374623,0.0007367265,0.00001676125,0.008367324,0.03298095,0.09118434,0.06440321,0.02246071,0.7778361],"study_design_scores_gemma":[0.004689909,0.001451894,0.07511507,0.0003734796,0.0003459555,0.00009654375,0.0008627573,0.3253771,0.2131828,0.1099957,0.265115,0.003393832],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1797027,0.00002114926,0.8130683,0.0002759705,0.000615812,0.00008425738,0.000009298346,0.0007929401,0.005429472],"genre_scores_gemma":[0.9983537,0.0000662471,0.001222728,0.0001850714,0.00006089135,0.000007158428,0.000002552482,0.00001669247,0.00008495059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.818651,"threshold_uncertainty_score":0.5577464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01943536739836479,"score_gpt":0.2336562823939826,"score_spread":0.2142209149956178,"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."}}