{"id":"W4236565392","doi":"10.1145/2070781.2024196","title":"Artist friendly facial animation retargeting","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Retargeting; Computer science; Animation; Computer facial animation; Key frame; Computer graphics (images); Workflow; Key (lock); Facial motion capture; Artificial intelligence; Computer animation; Computer vision; Character animation; Process (computing); Set (abstract data type); Human–computer interaction; Frame (networking); Facial recognition system; Pattern recognition (psychology); Programming language","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":[0.0004027883,0.0007697248,0.000410613,0.0005190555,0.0003139252,0.0004564923,0.0009714641,0.0005794369,0.0107259],"category_scores_gemma":[0.001419147,0.0003387851,0.0005241098,0.0001738487,0.0002969888,0.0006761792,0.001169206,0.0006739615,0.002313894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002008783,"about_ca_system_score_gemma":0.0001845595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006196703,"about_ca_topic_score_gemma":0.0009151428,"domain_scores_codex":[0.9996793,0.00005132635,0.00001469986,0.00007857324,0.0001424409,0.00003375753],"domain_scores_gemma":[0.9995912,0.0001384012,0.00003247484,0.0001445344,0.00006080521,0.0000325765],"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":[0.0002742957,0.0001162242,0.0005588444,0.000208208,0.00004405753,0.0005834262,0.0006871878,0.03278136,0.5483264,0.007236026,0.008109466,0.4010746],"study_design_scores_gemma":[0.00009677704,0.0003590829,0.002382256,0.00005836767,0.00007006097,0.001806145,0.0001744906,0.5946538,0.2869859,0.007005837,0.106287,0.0001202127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03618679,0.0001712307,0.9410875,0.0001035634,0.0001117281,0.0001237157,0.00008549516,0.01164131,0.0104887],"genre_scores_gemma":[0.3342077,0.0003580427,0.6394344,0.0001719414,0.00007909586,0.000166032,0.0003736163,0.003306186,0.02190305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0107259,"threshold_uncertainty_score":0.03588164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137706771161896,"score_gpt":0.2219536627693051,"score_spread":0.1905765950576861,"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."}}