{"id":"W2987886924","doi":"10.1145/3355089.3356536","title":"DReCon","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ubisoft (Canada); McGill University","funders":"Mitacs; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion capture; Character animation; Animation; Kinematics; Reinforcement learning; Controller (irrigation); Motion (physics); Artificial intelligence; Inverse kinematics; Matching (statistics); Character (mathematics); Trajectory; Computer vision; Computer animation; Simulation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006305457,0.001420964,0.0009996283,0.001198172,0.001085963,0.002726224,0.001926681,0.00162337,0.4406126],"category_scores_gemma":[0.001473811,0.0005424801,0.0007468053,0.0007837755,0.0004411606,0.001690035,0.002870914,0.001673867,0.2551858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008977019,"about_ca_system_score_gemma":0.0009038924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003079159,"about_ca_topic_score_gemma":0.003783658,"domain_scores_codex":[0.9993892,0.00006989079,0.00002970432,0.0001892384,0.0002214558,0.000100562],"domain_scores_gemma":[0.9992648,0.00009754075,0.00003132132,0.0002347015,0.0002178174,0.0001537636],"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":[0.001219546,0.0003506189,0.002029538,0.0006255403,0.00006260931,0.0006552805,0.000241156,0.006829602,0.01658808,0.03673458,0.455998,0.4786654],"study_design_scores_gemma":[0.0001249768,0.0001171497,0.0009039901,0.00007819521,0.00002077055,0.000319529,0.00007126902,0.01068026,0.005560711,0.006901914,0.9751844,0.00003696073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01133391,0.001888831,0.09871047,0.002171636,0.001877248,0.000492645,0.01523905,0.05520416,0.813082],"genre_scores_gemma":[0.1034971,0.001619291,0.04915351,0.001743271,0.0003229278,0.0006471358,0.03048608,0.01030676,0.8022239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4406126,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256230773080076,"score_gpt":0.2085236616306879,"score_spread":0.1959613538998872,"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."}}