{"id":"W2913999503","doi":"10.1145/3301411","title":"Perceptual Effects of Inconsistency in Human Animations","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Human Motion and Animation","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Nvidia","keywords":"Motion (physics); Motion capture; Perception; Animation; Kinematics; Consistency (knowledge bases); Psychology; Biological motion; Action (physics); Character animation; Attractiveness; Cognitive psychology; Communication; Computer science; Artificial intelligence; Computer vision; Computer animation; Computer graphics (images); Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00148796,0.0004545786,0.0003455161,0.0006721366,0.0002048879,0.0008160959,0.0003430304,0.0005574857,0.001956433],"category_scores_gemma":[0.01647523,0.0003816103,0.0003291229,0.0001572226,0.0004712065,0.0005913966,0.001483695,0.0005733704,0.00009580129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000186243,"about_ca_system_score_gemma":0.00007267829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002563806,"about_ca_topic_score_gemma":0.0001799147,"domain_scores_codex":[0.9984323,0.0006217124,0.0001221349,0.0002719661,0.0004678582,0.00008402407],"domain_scores_gemma":[0.9909813,0.005722405,0.001503593,0.0009123966,0.0004939037,0.0003863292],"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":[0.004341832,0.0001033806,0.04980569,0.0003971596,0.0003138559,0.0007653356,0.003269381,0.004827128,0.8987453,0.0008344454,0.000248177,0.03634832],"study_design_scores_gemma":[0.0002293873,0.003698729,0.8679138,0.000117917,0.0004024955,0.003148388,0.002590935,0.02829171,0.08911452,0.002253678,0.00205193,0.0001864708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944817,0.00008593152,0.004307125,0.00002689886,0.00001142773,0.00001614166,0.00003014036,0.00005139212,0.000989277],"genre_scores_gemma":[0.9972205,0.00002235468,0.002541937,0.00001631589,0.000006998541,0.000007333143,0.00004589968,0.00002469861,0.0001139354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001956433,"threshold_uncertainty_score":0.007869184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007841973152895062,"score_gpt":0.2188632390358455,"score_spread":0.2110212658829504,"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."}}