{"id":"W2804619907","doi":"10.1145/3197517.3201292","title":"Visemenet","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":227,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Massachusetts; National Science Foundation","keywords":"Computer science; Computer facial animation; Viseme; Animation; Speech recognition; Motion (physics); Artificial intelligence; Motion capture; Synchronization (alternating current); Face (sociological concept); Computer animation; Speech synthesis; Computer graphics (images); Speech technology","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":[0.0001027855,0.00009026991,0.00006818432,0.0001685351,0.0003400945,0.0001073293,0.0007656469,0.00005100388,0.00004921091],"category_scores_gemma":[0.00001248362,0.00008440942,0.00005784523,0.0007402137,0.00008676899,0.0003142305,0.000008178867,0.0001366334,0.0001369056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009877999,"about_ca_system_score_gemma":0.00003786772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005247682,"about_ca_topic_score_gemma":0.00002771639,"domain_scores_codex":[0.9992448,0.00001728441,0.0001060434,0.0002449212,0.0001866142,0.0002003203],"domain_scores_gemma":[0.9990891,0.00004700147,0.00002904049,0.0006815474,0.00007939835,0.00007389609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001858368,0.000298859,0.0002809627,0.00001715398,0.00006445382,0.00001226863,0.0008226868,0.0000453524,0.006913611,0.007915901,0.001953054,0.9816571],"study_design_scores_gemma":[0.0007702077,0.0007744542,0.002472782,0.00006745239,0.00002708327,0.00004747917,0.00004451981,0.003549055,0.812388,0.1253802,0.05388452,0.0005942679],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009109681,0.00003012892,0.9862418,0.002419757,0.0004038913,0.0000453483,0.00000170122,0.0002440445,0.001503695],"genre_scores_gemma":[0.8850074,0.00004013105,0.112231,0.002464537,0.00007385357,0.000008394962,4.624679e-7,0.000007955994,0.0001662855],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9810628,"threshold_uncertainty_score":0.3442116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182020117249972,"score_gpt":0.2660231279796109,"score_spread":0.2442029268071111,"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."}}