{"id":"W1556553177","doi":"10.1002/cav.1471","title":"Optimized keyframe extraction for 3D character animations","year":2012,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Human Motion and Animation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University; Carnegie Mellon University","keywords":"Computer science; Animation; Embedding; Character animation; Character (mathematics); Skeletal animation; Artificial intelligence; Dimension (graph theory); Computer animation; Carry (investment); Computer vision; Computer graphics (images); Computer facial animation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001754281,0.0001365847,0.0001377094,0.0001381669,0.0001417918,0.0001112865,0.00004620781,0.00006948475,0.0002419658],"category_scores_gemma":[0.000009212123,0.000141083,0.0000489156,0.0001024421,0.00001774803,0.0007726533,0.0000141476,0.00009346418,0.0001112934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003841799,"about_ca_system_score_gemma":0.000003908938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.97009e-7,"about_ca_topic_score_gemma":6.611352e-7,"domain_scores_codex":[0.9993237,0.00002288019,0.000243091,0.0001135483,0.00009404089,0.0002027221],"domain_scores_gemma":[0.9996459,0.00006391248,0.00004475613,0.00008949247,0.00005034575,0.0001055644],"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":[0.0001705545,0.0006488062,0.001440555,0.0006114218,0.0002756084,0.000001031649,0.01176984,0.02268944,0.03480602,0.1737759,0.06765042,0.6861604],"study_design_scores_gemma":[0.0007725965,0.00007342662,0.0362857,0.00003036619,0.00002285336,0.000009349259,0.00005467376,0.9021529,0.0003307737,0.00008402824,0.05993323,0.0002501245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06599102,0.00006883813,0.9313089,0.0001133753,0.0006831244,0.0002727385,0.0000121236,0.0003828645,0.001167003],"genre_scores_gemma":[0.9197643,0.00003531366,0.0785747,0.0002438833,0.0007784404,0.00005526002,0.0001293164,0.0000292075,0.0003895049],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8794634,"threshold_uncertainty_score":0.5753198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02035898630112595,"score_gpt":0.2616962205712332,"score_spread":0.2413372342701073,"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."}}