{"id":"W2952270885","doi":"10.48550/arxiv.1705.03098","title":"A simple yet effective baseline for 3d human pose estimation","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Pose; Computer science; Artificial intelligence; Benchmark (surveying); Ground truth; Deep learning; Baseline (sea); Task (project management); Surprise; Set (abstract data type); 3D pose estimation; Convolutional neural network; Word error rate; Computer vision; Pixel; Pattern recognition (psychology); Geography; Cartography; Engineering","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":[0.001036366,0.003470435,0.001392259,0.001457906,0.0008473982,0.001931342,0.002210498,0.002079481,0.0282909],"category_scores_gemma":[0.004079205,0.0009851974,0.001295535,0.001142415,0.0009415146,0.002202196,0.003861228,0.002254501,0.02580786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009455929,"about_ca_system_score_gemma":0.001580444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008746661,"about_ca_topic_score_gemma":0.01680373,"domain_scores_codex":[0.9980155,0.0002138429,0.00006115413,0.0009914947,0.0005173571,0.0002005455],"domain_scores_gemma":[0.9991166,0.0001153703,0.00004851857,0.0003748175,0.0002651158,0.00007955553],"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":[0.0006760783,0.0003668637,0.003037334,0.0005985111,0.0002293658,0.0002744898,0.0001252875,0.03217435,0.06021707,0.009315824,0.1150455,0.7779393],"study_design_scores_gemma":[0.0001579348,0.000829955,0.0160342,0.0004730421,0.0001679107,0.001603964,0.0002103564,0.6389164,0.09804385,0.03507019,0.2082996,0.0001925289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01814757,0.003349903,0.888446,0.0006945116,0.002055556,0.0002816996,0.01414181,0.04588685,0.02699611],"genre_scores_gemma":[0.2888551,0.002321866,0.6080938,0.001580853,0.0006818732,0.0007579903,0.05432326,0.003053462,0.04033178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0282909,"threshold_uncertainty_score":0.09464246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07424930410867034,"score_gpt":0.2382774319782042,"score_spread":0.1640281278695339,"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."}}