{"id":"W2104425538","doi":"10.1145/1778765.1778778","title":"High resolution passive facial performance capture","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Face recognition and analysis","field":"Computer Science","cited_by":235,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Polygon mesh; Computer vision; Artificial intelligence; Texture mapping; Computer graphics (images); Face (sociological concept); Parameterized complexity; Sequence (biology); Tracking (education); Texture (cosmology); Image (mathematics); Algorithm","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":[0.0002272238,0.0005042655,0.0003971249,0.000402962,0.0002254279,0.0007031752,0.0009491402,0.000543452,0.009629158],"category_scores_gemma":[0.0007472913,0.0004426966,0.0003164918,0.0002224219,0.0002038796,0.0006949225,0.001189503,0.0005501059,0.002655959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003160522,"about_ca_system_score_gemma":0.000379711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389431,"about_ca_topic_score_gemma":0.001963799,"domain_scores_codex":[0.9996318,0.00002930867,0.000009247316,0.00008310766,0.0002032494,0.00004325464],"domain_scores_gemma":[0.9997657,0.00004406435,0.0000163874,0.00008217966,0.00007160855,0.00002014894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002383809,0.0001103876,0.001912943,0.0001979024,0.00003355181,0.0003987635,0.0001763029,0.01024364,0.7814971,0.004909114,0.00975072,0.1905313],"study_design_scores_gemma":[0.0001238764,0.0007821055,0.02233573,0.0001261815,0.0001077683,0.00736652,0.0002411554,0.2953956,0.5672935,0.005741658,0.1003152,0.0001706929],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0440008,0.0003350661,0.9358194,0.000207875,0.00015278,0.0001784208,0.0006882866,0.001615755,0.01700155],"genre_scores_gemma":[0.4502205,0.0008446773,0.5093852,0.0004793511,0.000204116,0.0004031009,0.001972938,0.0006039049,0.03588611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009629158,"threshold_uncertainty_score":0.03221273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178120293641957,"score_gpt":0.2217284316932933,"score_spread":0.2099472287568737,"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."}}