{"id":"W4401541958","doi":"10.1016/j.patcog.2024.110891","title":"Maskrenderer: 3D-infused multi-mask realistic face reenactment","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face recognition and analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Health Economics","funders":"","keywords":"Computer science; Face (sociological concept); Computer vision; Artificial intelligence; Computer graphics (images)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002789564,0.0002269316,0.0002032881,0.0003106321,0.0001148663,0.0004941637,0.0003278086,0.00009583158,0.0005122],"category_scores_gemma":[0.00003999185,0.0002105515,0.0001771155,0.000522068,0.00002783317,0.0005308325,0.0001071484,0.000214945,0.004161873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080485,"about_ca_system_score_gemma":0.00004414547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000113891,"about_ca_topic_score_gemma":0.00007313043,"domain_scores_codex":[0.9982013,0.0001273196,0.0003397073,0.0006441709,0.0003472618,0.0003402619],"domain_scores_gemma":[0.9991957,0.0001276453,0.00007120537,0.0003706677,0.00008397047,0.0001508347],"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":[0.000002006309,0.00009496226,0.00008627216,0.00009088462,0.0000866991,0.00008531805,0.0004838402,0.00002637098,0.0004916976,0.00003908804,0.001138217,0.9973747],"study_design_scores_gemma":[0.0008666796,0.0001212468,0.0008908679,0.0005521058,0.0001651442,0.00008173331,0.0002287426,0.9809409,0.0043903,0.00238706,0.008582841,0.0007923807],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005489552,0.0003260416,0.9891096,0.001704264,0.0006323888,0.0002109585,0.0001088841,0.0008089787,0.001609287],"genre_scores_gemma":[0.9800925,0.0003834528,0.01688983,0.001118836,0.0001599942,0.00009655987,0.0003917786,0.00003331254,0.0008336775],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9965823,"threshold_uncertainty_score":0.9966135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05102618048917607,"score_gpt":0.2792692816790674,"score_spread":0.2282431011898913,"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."}}