{"id":"W2770331385","doi":"10.1145/3130800.3130830","title":"Online generative model personalization for hand tracking","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Computer science; Workflow; Tracking (education); Generative model; Personalization; Frame (networking); Key frame; Artificial intelligence; Computer vision; Session (web analytics); Novelty; Key (lock); Motion (physics); Software; Generative grammar; Database","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":[0.001193516,0.001768033,0.001893949,0.001619409,0.0007638519,0.001672656,0.002351231,0.001887528,0.007231812],"category_scores_gemma":[0.005009331,0.001476937,0.002052658,0.001793852,0.001121277,0.001850378,0.00306106,0.002719529,0.00443899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001369157,"about_ca_system_score_gemma":0.001206512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009889735,"about_ca_topic_score_gemma":0.02297696,"domain_scores_codex":[0.9986922,0.0002234027,0.00005491986,0.0005477401,0.0003588962,0.000122874],"domain_scores_gemma":[0.9982564,0.0007447746,0.0001239991,0.0006127477,0.0001748167,0.00008728603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001719849,0.0000995695,0.001462017,0.000110122,0.0001447711,0.0001878193,0.0002303059,0.6093986,0.01056322,0.0127458,0.008820823,0.356065],"study_design_scores_gemma":[0.000008741759,0.00001017802,0.000147036,0.000008515381,0.0000117915,0.00007865326,0.000009366794,0.9897494,0.00151527,0.006558083,0.001890415,0.00001257406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002727213,0.0001779211,0.9936432,0.00006742735,0.00003020612,0.00002157759,0.0001023695,0.002237947,0.0009921091],"genre_scores_gemma":[0.348292,0.000759789,0.6323248,0.0006393437,0.0002381923,0.0003242819,0.001873354,0.002425471,0.0131229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009889735,"threshold_uncertainty_score":0.02419281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07691448432311974,"score_gpt":0.3007052913103499,"score_spread":0.2237908069872301,"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."}}