{"id":"W4388010869","doi":"10.1145/3623264.3624438","title":"Generating Emotionally Expressive Look-At Animation","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Motion and Animation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ubisoft (Canada)","funders":"","keywords":"Computer science; Animation; Character animation; Computer facial animation; Component (thermodynamics); Context (archaeology); Skeletal animation; Human–computer interaction; Motion (physics); Adaptation (eye); Quality (philosophy); Computer animation; Artificial intelligence; Computer graphics (images); Psychology","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":["insufficient_payload"],"category_scores_codex":[0.00005713038,0.00005406183,0.00004255468,0.00006674817,0.00007789871,0.00002871463,0.00003218761,0.00002750908,0.001201968],"category_scores_gemma":[0.0000123015,0.00005414361,0.00002088576,0.0001065525,0.000004933289,0.0001203202,0.00001421408,0.0000319698,0.002236971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003928329,"about_ca_system_score_gemma":0.000002597566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.078126e-7,"about_ca_topic_score_gemma":0.000007175084,"domain_scores_codex":[0.999626,0.00000770268,0.0001055777,0.00006959047,0.00009807962,0.0000930575],"domain_scores_gemma":[0.9998709,0.00001230063,0.00001149853,0.00005612384,0.00002187786,0.00002732045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001041465,0.000006854644,0.0002659934,0.00005410741,0.00001481945,0.000003884644,0.0007266271,0.3415467,0.5640572,0.008272506,0.0795893,0.005461017],"study_design_scores_gemma":[0.0001309213,0.000006629495,0.008262804,0.00001289629,0.000001975591,0.000002098736,0.00008666168,0.9763129,0.0117103,0.0002325492,0.003137538,0.0001027241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9316887,0.00001236659,0.02575016,0.0001023772,0.0001661599,0.00007337493,0.000003768662,0.001416293,0.04078684],"genre_scores_gemma":[0.9908329,0.00001105162,0.001650674,0.00006336888,0.0001308995,0.00000987374,0.0000917139,0.00001573604,0.007193747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6347662,"threshold_uncertainty_score":0.9997111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826022862161126,"score_gpt":0.2262412093622286,"score_spread":0.2079809807406174,"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."}}