{"id":"W4312465973","doi":"10.1109/cvpr52688.2022.00664","title":"MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Pose; Artificial intelligence; Monocular; Latency (audio); Bundle adjustment; Task (project management); Calibration; Task analysis; Computer vision; Machine learning; Pattern recognition (psychology); Mathematics; Engineering; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008436856,0.00446303,0.002469346,0.001956194,0.000563133,0.001730007,0.004053903,0.001614635,0.01166011],"category_scores_gemma":[0.002286931,0.0018422,0.002368289,0.002027146,0.0007455518,0.002997421,0.004486595,0.003273284,0.01378432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000616585,"about_ca_system_score_gemma":0.001373657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007480326,"about_ca_topic_score_gemma":0.02252494,"domain_scores_codex":[0.9984668,0.0001493056,0.00003697827,0.00065151,0.0005721354,0.0001232171],"domain_scores_gemma":[0.9991881,0.00009681174,0.00006449206,0.0004511945,0.0001308373,0.00006861537],"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":[0.0005981994,0.0003631059,0.00194496,0.000348184,0.0004460341,0.0002529689,0.0001398221,0.04810981,0.02300193,0.004033747,0.09676124,0.8239999],"study_design_scores_gemma":[0.0001462158,0.0003451537,0.00240453,0.00009586198,0.00009100246,0.0009299271,0.0001216487,0.911404,0.02348563,0.01710163,0.04377135,0.0001030279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007719799,0.00105691,0.9184826,0.0001891828,0.0002761502,0.0002056743,0.0054526,0.06405552,0.00256162],"genre_scores_gemma":[0.1335258,0.001254599,0.8125288,0.0004691028,0.0002634416,0.0004013003,0.038667,0.00356418,0.009325762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01166011,"threshold_uncertainty_score":0.03900701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06631774104697905,"score_gpt":0.2829277449719134,"score_spread":0.2166100039249344,"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."}}