{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006006285,0.0009548459,0.0006390532,0.0005292219,0.0002857282,0.0006904076,0.0006572317,0.0007589108,0.002331149],"category_scores_gemma":[0.002702087,0.0006362637,0.0004966562,0.0004722197,0.0005978281,0.0008184185,0.001159779,0.00095179,0.0007481053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006308501,"about_ca_system_score_gemma":0.0008376433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001619136,"about_ca_topic_score_gemma":0.002768619,"domain_scores_codex":[0.9995703,0.0001123631,0.00001993541,0.000105844,0.0001534117,0.00003819374],"domain_scores_gemma":[0.9994936,0.0002075821,0.00007485988,0.0001203398,0.00007307083,0.00003063834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002028063,0.0000628386,0.001042505,0.0001538087,0.00004458075,0.0001184361,0.00022362,0.6788749,0.09370464,0.009141756,0.001739873,0.2146902],"study_design_scores_gemma":[0.00001095033,0.00005287675,0.0003384316,0.0000103729,0.000006320745,0.00006718361,0.00003509368,0.9750505,0.01977166,0.002890116,0.001751649,0.00001483281],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01042159,0.00006695368,0.9882045,0.00003799548,0.00001198105,0.0000253787,0.00003310162,0.0006313142,0.0005671885],"genre_scores_gemma":[0.3267325,0.0001662473,0.6703708,0.00006397838,0.00001384293,0.0001095708,0.0002514386,0.0005780266,0.001713676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002331149,"threshold_uncertainty_score":0.007798433,"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."}}