{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005465385,0.00009553824,0.00007919158,0.00009747866,0.0007743843,0.0001404283,0.0001411314,0.0000705984,0.00002377111],"category_scores_gemma":[0.00002145711,0.000101938,0.00008046636,0.00004089417,0.00004402633,0.0002586579,8.944456e-7,0.0001093862,0.000003489646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002128331,"about_ca_system_score_gemma":0.000009205555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002227614,"about_ca_topic_score_gemma":0.0001834348,"domain_scores_codex":[0.9995797,0.000006044878,0.0001116858,0.0001111439,0.00008706475,0.0001043915],"domain_scores_gemma":[0.9995707,0.00002211888,0.00002982731,0.000268456,0.00007173837,0.00003714947],"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":[0.00003211385,0.0003034648,0.00006101895,0.0001820772,0.0001492431,9.020764e-7,0.003142666,0.9060681,0.0214582,0.01185879,0.0006482877,0.0560951],"study_design_scores_gemma":[0.0004091675,0.00003143177,0.001029191,0.00002894539,0.00002796885,8.393567e-7,0.00005300176,0.9881205,0.004838662,0.004872864,0.0004577646,0.0001296912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05036075,0.00002035725,0.9486218,0.0003662035,0.0001501735,0.0001410123,0.0001067593,0.0001132593,0.0001197037],"genre_scores_gemma":[0.9900029,0.0001043165,0.009428974,0.0001154789,0.00005668338,0.00002956532,0.00004862048,0.00002563286,0.0001878342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9396421,"threshold_uncertainty_score":0.5956016,"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."}}