{"id":"W51279807","doi":"","title":"FACIAL ANIMATION WITH MOTION CAPTURE BASED ON SURFACE BLENDING","year":2017,"lang":"en","type":"article","venue":"International Conference on Computer Graphics Theory and Applications","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Facial motion capture; Computer vision; Computer science; Artificial intelligence; Motion capture; Computer facial animation; Animation; Facial expression; Feature (linguistics); Computer animation; Focus (optics); Face (sociological concept); Surface (topology); Expression (computer science); Point (geometry); Computer graphics (images); Motion (physics); Point cloud; Facial recognition system; Feature extraction; Mathematics; Face detection; Optics","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":[0.0003113258,0.0001520379,0.0001150572,0.000165918,0.0006480455,0.0007569811,0.0007502025,0.0000612024,0.00003613283],"category_scores_gemma":[0.00001261953,0.0001296209,0.00005527727,0.0001127366,0.0001298811,0.0003829872,0.00008005359,0.0001808282,0.00003009266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001951249,"about_ca_system_score_gemma":0.00003225189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008328257,"about_ca_topic_score_gemma":0.00001101207,"domain_scores_codex":[0.9989407,0.00007889202,0.0001477807,0.0004149432,0.0002945484,0.0001230999],"domain_scores_gemma":[0.9988977,0.0001229783,0.0001912947,0.0004818145,0.0002286166,0.00007757868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002251995,0.00007926712,0.0003864013,0.000003781457,0.0000275653,0.000001450836,0.00004991629,0.0008501699,0.0000572246,0.9705493,0.00002157888,0.02795078],"study_design_scores_gemma":[0.0004861691,0.00008397896,0.004398418,0.00008042262,0.00001277955,0.000005254805,0.00002648561,0.8942167,0.0003181415,0.09860973,0.00151952,0.0002423989],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006498934,0.00000315646,0.9810824,0.003019261,0.000110559,0.000178739,0.00002933728,0.00009072286,0.008986915],"genre_scores_gemma":[0.9934231,0.00002400303,0.005578781,0.0006751509,0.00008657825,0.00004067188,0.00004279479,0.000006557485,0.0001223586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9869242,"threshold_uncertainty_score":0.7299587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02862089099017041,"score_gpt":0.2823401209317764,"score_spread":0.253719229941606,"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."}}