{"id":"W4386212320","doi":"10.1109/tvcg.2023.3309107","title":"Motion In-Betweening via Deep -Interpolator","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Mitacs","keywords":"Computer science; Computer vision; Computer graphics (images); Artificial intelligence; Motion (physics); Visualization","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":[0.0006524271,0.0008360772,0.000718986,0.0004364794,0.0002841438,0.0005802313,0.001194644,0.0007386982,0.004108963],"category_scores_gemma":[0.001802763,0.0002656709,0.0005335049,0.0004918398,0.0004637579,0.0008777485,0.001507746,0.001515421,0.001085652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005029845,"about_ca_system_score_gemma":0.000948319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00533705,"about_ca_topic_score_gemma":0.007795961,"domain_scores_codex":[0.999714,0.00004369463,0.00001334875,0.0001028678,0.00008361178,0.00004250712],"domain_scores_gemma":[0.9997112,0.0001029825,0.00003425297,0.00008479341,0.00004015979,0.00002657503],"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":[0.0006862367,0.000139838,0.001438594,0.0002154964,0.00007947502,0.0002146695,0.0001486238,0.3909422,0.03364129,0.01264249,0.00578305,0.554068],"study_design_scores_gemma":[0.00001321063,0.00004984918,0.0001533755,0.00001176945,0.00000786389,0.00004562428,0.00001103562,0.9840468,0.01029346,0.003412951,0.001946632,0.000007510784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03537721,0.0005775245,0.956052,0.0001890443,0.0001615159,0.00005614002,0.0003125609,0.004950889,0.002323071],"genre_scores_gemma":[0.6056373,0.0004018001,0.3855031,0.00020954,0.00006831399,0.00007654993,0.001327625,0.0004158807,0.006359891],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00533705,"threshold_uncertainty_score":0.01374584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01556559556321457,"score_gpt":0.2469078031478097,"score_spread":0.2313422075845951,"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."}}