{"id":"W4367662933","doi":"10.1109/vrw58643.2023.00244","title":"F2RPC: Fake to Real Portrait Control from a Virtual Character","year":2023,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Portrait; Computer science; Character (mathematics); Task (project management); Artificial intelligence; Virtual reality; Face (sociological concept); Computer graphics (images); Computer vision; Image (mathematics); Motion (physics); Human–computer interaction; Art; Visual arts; Mathematics; Engineering","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":[0.000685413,0.0008048008,0.0005243391,0.0004037447,0.0003574406,0.0007109569,0.001113612,0.0007961569,0.005909405],"category_scores_gemma":[0.002572546,0.0002850069,0.0005734218,0.0001905908,0.001010426,0.0009894897,0.001255946,0.001066193,0.00110309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004344429,"about_ca_system_score_gemma":0.0002555382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001111318,"about_ca_topic_score_gemma":0.0009937785,"domain_scores_codex":[0.9993047,0.0001637855,0.00001758378,0.0001967422,0.0002448629,0.00007234221],"domain_scores_gemma":[0.9993153,0.0002148349,0.00006203054,0.0002923822,0.00007082448,0.0000446317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007919967,0.0001857633,0.001220851,0.0002637308,0.0001255221,0.001113518,0.0004672215,0.2787628,0.1144166,0.03049252,0.009526942,0.5626324],"study_design_scores_gemma":[0.00002338323,0.0001955436,0.0005190936,0.00002248265,0.00002555436,0.0007364494,0.00004134376,0.9414713,0.04450509,0.00443281,0.007996649,0.00003023618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06552324,0.0004202184,0.9203348,0.0004162032,0.0002575774,0.0001652495,0.0001000039,0.002289669,0.01049291],"genre_scores_gemma":[0.777645,0.000283598,0.2081256,0.000317043,0.0001123379,0.00009150722,0.0002375387,0.0003505021,0.01283702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005909405,"threshold_uncertainty_score":0.01976895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193717505343872,"score_gpt":0.2255630080263195,"score_spread":0.2136258329728808,"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."}}