{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003860752,0.001245552,0.001101588,0.002850716,0.0003901433,0.0007282146,0.0007394701,0.000670554,0.003879108],"category_scores_gemma":[0.00176821,0.00058537,0.000688906,0.001386465,0.0003752184,0.0009370691,0.0008310897,0.0006438866,0.001283233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004394086,"about_ca_system_score_gemma":0.0004751203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002259401,"about_ca_topic_score_gemma":0.002742415,"domain_scores_codex":[0.9996054,0.00007195993,0.00002288423,0.0001125058,0.0001406775,0.00004664618],"domain_scores_gemma":[0.9995745,0.0001585716,0.00005019268,0.00007251205,0.0001157134,0.00002853822],"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":[0.0004163595,0.00005018862,0.0005794024,0.0003562283,0.00006886439,0.0001839535,0.000160881,0.04162786,0.2021406,0.004116526,0.004045245,0.7462539],"study_design_scores_gemma":[0.00003727192,0.0001010455,0.001653434,0.00003597762,0.0000496573,0.0003331185,0.00007824419,0.8603744,0.1200084,0.005166109,0.01212692,0.00003553446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01399507,0.0003745526,0.9825257,0.00004166711,0.00005220324,0.0000623164,0.0001656745,0.002211638,0.0005712286],"genre_scores_gemma":[0.174946,0.0004908055,0.821435,0.0000288937,0.00004506929,0.00009753235,0.0008769971,0.0005866006,0.001493119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003879108,"threshold_uncertainty_score":0.01297688,"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."}}