{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001901401,0.001697092,0.0004700589,0.0009497915,0.000425478,0.001518441,0.001876544,0.001251233,0.06896371],"category_scores_gemma":[0.0008879898,0.0004585867,0.0007338042,0.0007462526,0.0002899053,0.002411589,0.001415793,0.001388834,0.02514182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007249311,"about_ca_system_score_gemma":0.0006575405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005989714,"about_ca_topic_score_gemma":0.01624356,"domain_scores_codex":[0.9997781,0.00001766727,0.000009772483,0.000119086,0.00004449304,0.00003092186],"domain_scores_gemma":[0.9998863,0.0000240006,0.000007298956,0.00003685732,0.00002964165,0.0000159233],"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":[0.0005164667,0.0002184513,0.001139036,0.0006330134,0.0001572472,0.0003804408,0.0001081709,0.02011381,0.0217973,0.01800973,0.2802603,0.6566659],"study_design_scores_gemma":[0.0001535234,0.0003555627,0.001872877,0.0001790138,0.0001235812,0.0007191139,0.0001917121,0.3916874,0.04922708,0.04341967,0.5119753,0.00009518883],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.05764337,0.004524761,0.4180222,0.001829653,0.00361164,0.0005130939,0.07159074,0.2521358,0.1901288],"genre_scores_gemma":[0.2864982,0.002126344,0.3915715,0.002941011,0.0003679871,0.0007264282,0.1651426,0.007865113,0.1427609],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06896371,"threshold_uncertainty_score":0,"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."}}