{"id":"W2158638750","doi":"10.1109/cvpr.2005.132","title":"Discriminative Density Propagation for 3D Human Motion Estimation","year":2005,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":219,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Artificial intelligence; Inference; Computer science; Generative model; Motion capture; Pattern recognition (psychology); Probabilistic logic; Bayesian inference; Conditional probability distribution; Belief propagation; Machine learning; Computer vision; Bayesian probability; Generative grammar; Motion (physics); Mathematics; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001403608,0.00006430643,0.00005721626,0.00007496579,0.0002353915,0.00009843447,0.00009771426,0.00003155628,0.00003583442],"category_scores_gemma":[0.00001615943,0.00005788553,0.00003218535,0.00007931445,0.00001293354,0.001161413,0.00002212378,0.00003741051,0.00008890948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005820856,"about_ca_system_score_gemma":0.000009684994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006971632,"about_ca_topic_score_gemma":0.00003816422,"domain_scores_codex":[0.9994462,0.0000231683,0.0001323054,0.0001874395,0.0001116625,0.00009927335],"domain_scores_gemma":[0.999624,0.00002216694,0.00006888525,0.0001199693,0.0001358011,0.00002918552],"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":[0.000002297961,0.00008493719,0.00002163844,0.00001242968,0.000004509785,1.671593e-7,0.0004942318,0.0001906553,0.002767892,0.1531564,0.0007435127,0.8425214],"study_design_scores_gemma":[0.0003848973,0.00009527528,0.00456161,0.00001242956,0.00001027609,0.000004474363,0.00002913337,0.8857743,0.08969603,0.01744391,0.001837391,0.0001503114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04509163,0.000001326505,0.9510351,0.0008606091,0.00007263685,0.000293221,8.874067e-7,0.0001729822,0.00247165],"genre_scores_gemma":[0.7778782,3.745328e-7,0.2209322,0.0001701182,0.00008971991,0.00003655724,0.00003325212,0.000003106796,0.0008564778],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8855836,"threshold_uncertainty_score":0.2360504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03055082250475222,"score_gpt":0.2931255146242817,"score_spread":0.2625746921195295,"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."}}