{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00003749775,0.00006515702,0.00005604595,0.0001253854,0.00004140723,0.00001656902,0.0000822338,0.00005230621,0.001004357],"category_scores_gemma":[0.000001755058,0.000069354,0.00005473643,0.0001518835,0.000008814444,0.0000918795,4.126584e-7,0.0001477043,0.0008909394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001330666,"about_ca_system_score_gemma":0.000002854363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001244009,"about_ca_topic_score_gemma":0.00001180719,"domain_scores_codex":[0.9996739,0.000006730556,0.00008350106,0.00007441233,0.0000742146,0.0000872618],"domain_scores_gemma":[0.9997057,0.00002304861,0.000006567485,0.0002201414,0.00001300802,0.00003147889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007096247,0.0007428892,0.003216401,0.0007723836,0.0006125755,0.000009353685,0.003805743,0.3394831,0.05425353,0.05633982,0.008524207,0.5321691],"study_design_scores_gemma":[0.006599464,0.001155335,0.09424017,0.0004873531,0.000227749,0.00006038135,0.001145151,0.3633123,0.08192663,0.05389228,0.3934081,0.003545079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7647668,0.00004194145,0.2167415,0.000347931,0.00115819,0.0002249031,0.0000159343,0.0009754745,0.01572733],"genre_scores_gemma":[0.9989182,0.00008441176,0.0003795413,0.0001455251,0.00001429226,0.000006332981,0.000004141414,0.00001590674,0.0004316943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.528624,"threshold_uncertainty_score":0.9999089,"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."}}