{"id":"W2894766094","doi":"10.1145/3272127.3275014","title":"SFV","year":2018,"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":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion capture; Character animation; Animation; Reinforcement learning; Artificial intelligence; Leverage (statistics); Computer animation; Motion (physics); Physics engine; Computer vision; Human–computer interaction; 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":[0.0005167378,0.001315962,0.0007475202,0.001255946,0.0008747386,0.002268034,0.001558761,0.002071713,0.5450225],"category_scores_gemma":[0.001678017,0.0003919241,0.0005813141,0.0009632077,0.000367147,0.001903554,0.002575272,0.001338462,0.3728812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008321528,"about_ca_system_score_gemma":0.0007856446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004899016,"about_ca_topic_score_gemma":0.005326657,"domain_scores_codex":[0.9995255,0.00004431376,0.00002342402,0.0001271861,0.0002149817,0.00006450024],"domain_scores_gemma":[0.9994424,0.00006190316,0.00002098138,0.0001750206,0.0001978033,0.0001019532],"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":[0.0001804229,0.0000612315,0.0005422666,0.0002384735,0.00001984394,0.0001312036,0.00006617472,0.002612833,0.005351358,0.01050557,0.722482,0.2578087],"study_design_scores_gemma":[0.00006598773,0.00004430756,0.0005812686,0.00008127489,0.000008127361,0.0001649299,0.00003578649,0.01478697,0.002699579,0.00958545,0.9719192,0.00002715688],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.004113991,0.001716972,0.1727788,0.002321835,0.003213224,0.0005464676,0.06728256,0.1275082,0.620518],"genre_scores_gemma":[0.09288549,0.002063816,0.09915601,0.002879864,0.001233015,0.0008650812,0.2141269,0.01896938,0.5678205],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4549775,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061558315292415,"score_gpt":0.2363071741584292,"score_spread":0.215691591005505,"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."}}