{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000388309,0.0002692548,0.0002928087,0.0002837263,0.0001742072,0.0002172077,0.001733865,0.0001259287,0.00007215943],"category_scores_gemma":[0.001390791,0.0002202499,0.0001824473,0.000344104,0.0002166734,0.0006906434,0.0003664428,0.0002761756,0.00002353983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196675,"about_ca_system_score_gemma":0.0001131362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003321383,"about_ca_topic_score_gemma":0.00001972982,"domain_scores_codex":[0.9980201,0.00001970535,0.0004818497,0.0006123469,0.0005971076,0.0002688809],"domain_scores_gemma":[0.9973884,0.0004756427,0.0005245304,0.0003093084,0.001135896,0.0001662361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001129929,0.002029913,0.006152072,0.0002177414,0.0006161339,0.000001267717,0.008397277,0.004490182,0.1090125,0.08074909,0.005171024,0.7820328],"study_design_scores_gemma":[0.001077287,0.00008568016,0.0009118164,0.0001102899,0.00003538665,0.000003195914,0.0003974211,0.9763392,0.01497717,0.005140975,0.0006916067,0.0002299795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06741663,0.00002670008,0.8842959,0.03565042,0.0008046778,0.002634091,0.0003795374,0.0003537747,0.008438298],"genre_scores_gemma":[0.9164582,0.00006639951,0.08199616,0.0003746846,0.000116728,0.0004300258,0.00005333538,0.0000168519,0.0004875864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.971849,"threshold_uncertainty_score":0.8981531,"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."}}