{"id":"W4233729960","doi":"10.1145/500213.500242","title":"Model-based face and lip animation for interactive virtual reality applications","year":2001,"lang":"en","type":"article","venue":"Proceedings of the ninth ACM international conference on Multimedia - MULTIMEDIA '01","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada; University of Ottawa","funders":"","keywords":"Animation; Computer science; Computer facial animation; Avatar; Computer graphics (images); Computer animation; Face (sociological concept); Virtual reality; Skeletal animation; Coding (social sciences); Track (disk drive); Facial motion capture; Multimedia; Human–computer interaction; Computer vision; Facial recognition system; Face detection; Feature extraction","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005280038,0.0003855774,0.0003316427,0.0003036206,0.0002009532,0.0005765065,0.0009363221,0.0006754517,0.007072975],"category_scores_gemma":[0.002126911,0.0002847369,0.0003339343,0.0001227878,0.0001919418,0.0007449605,0.0006558311,0.0005253557,0.001398754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000239701,"about_ca_system_score_gemma":0.0002334493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008639556,"about_ca_topic_score_gemma":0.0008310216,"domain_scores_codex":[0.9995806,0.0001159255,0.00001430644,0.00004680951,0.0002099274,0.00003241995],"domain_scores_gemma":[0.9995503,0.0002177583,0.00001941858,0.00009384828,0.00007893869,0.00003973449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008367551,0.0002615727,0.0007591689,0.000295996,0.00004296864,0.0002440792,0.0002623779,0.02655495,0.6663631,0.005886531,0.003479411,0.295013],"study_design_scores_gemma":[0.0001189087,0.001172853,0.002390092,0.00006607571,0.00007468063,0.001192414,0.0001032189,0.6347355,0.3360693,0.003129928,0.02084031,0.0001066533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07249297,0.0003917686,0.9174635,0.0001847084,0.000150156,0.000228985,0.0001301287,0.003788097,0.005169736],"genre_scores_gemma":[0.6432408,0.0004914914,0.3484877,0.0001128428,0.00005408505,0.0002497764,0.0003159192,0.0005049266,0.006542538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007072975,"threshold_uncertainty_score":0.02366149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.066002304303658,"score_gpt":0.3278013754030729,"score_spread":0.2617990710994149,"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."}}