{"id":"W1992343628","doi":"10.1109/tvcg.2013.84","title":"Marker Optimization for Facial Motion Acquisition and Deformation","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simon Fraser University","keywords":"Computer science; Motion capture; Artificial intelligence; Computer vision; Robustness (evolution); Skinning; Facial motion capture; Motion (physics); Pattern recognition (psychology); Facial recognition system; Face detection","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006828558,0.0001159142,0.0000999239,0.00023803,0.0001867748,0.0001190751,0.000024876,0.00009073763,0.00002834518],"category_scores_gemma":[6.242522e-7,0.0001198489,0.00004459259,0.0001947095,0.00001973105,0.0003449502,5.385031e-7,0.00005334121,0.000002925131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001443301,"about_ca_system_score_gemma":0.000002429443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006428716,"about_ca_topic_score_gemma":0.000003132002,"domain_scores_codex":[0.9994657,0.00002110714,0.0001864599,0.0001345936,0.00009053376,0.000101597],"domain_scores_gemma":[0.9997327,0.00002289888,0.00002682888,0.00006390546,0.0001013744,0.0000523365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008121755,0.00003809473,0.00003172324,0.00008845188,0.00004996264,5.108124e-8,0.0003486404,0.9458566,0.0000338659,0.002817439,0.0001138055,0.05061321],"study_design_scores_gemma":[0.0003159679,0.00004479471,0.0002251156,0.00001925448,0.00003749957,0.00000208579,0.00002607027,0.9986136,0.0002951738,0.0002631857,0.00002708276,0.0001301887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05344147,0.00002356109,0.9458883,0.00001894176,0.0001893004,0.0002163768,0.00001127038,0.0001969247,0.00001381321],"genre_scores_gemma":[0.9973529,0.0002933508,0.002004863,0.0001547888,0.00004361793,0.00005777719,0.00006212591,0.00001867159,0.00001193428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9439114,"threshold_uncertainty_score":0.4887295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031920212618644,"score_gpt":0.2219847050307856,"score_spread":0.2116655029045991,"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."}}