{"id":"W4388430467","doi":"10.1109/tmm.2023.3330522","title":"Realistic Depth Image Synthesis for 3D Hand Pose Estimation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pose; Computer vision; Artificial intelligence; Image (mathematics); 3D pose estimation","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":[0.0004575215,0.001308163,0.0007454313,0.0005122831,0.0001447405,0.0006305132,0.0008596703,0.0009288224,0.004394483],"category_scores_gemma":[0.002136949,0.0006316169,0.0007735058,0.0004851376,0.0004425649,0.0008761879,0.00107889,0.0009792397,0.001628555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004548996,"about_ca_system_score_gemma":0.0007130037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002402047,"about_ca_topic_score_gemma":0.00484087,"domain_scores_codex":[0.9994425,0.00008796024,0.00002274499,0.0001890283,0.0001915164,0.00006631714],"domain_scores_gemma":[0.9995561,0.0001446009,0.00005531988,0.0001356579,0.00008287076,0.0000254821],"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":[0.000614646,0.0001612792,0.001728771,0.0006387843,0.0001556457,0.0002860948,0.0001913309,0.3300171,0.1663685,0.004567109,0.008861477,0.4864092],"study_design_scores_gemma":[0.00002810304,0.0001304162,0.00140911,0.00004280828,0.00002713113,0.0003803533,0.00003483068,0.9493477,0.04068518,0.002824214,0.00506227,0.00002783551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01320816,0.0003681106,0.9817671,0.00007467707,0.00005997313,0.00008390404,0.0005403371,0.002430786,0.001466886],"genre_scores_gemma":[0.4193156,0.0007682116,0.5729966,0.0003317788,0.00008436168,0.000244469,0.002561409,0.0003749581,0.003322707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004394483,"threshold_uncertainty_score":0.01470095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03247850330242019,"score_gpt":0.2885760705584957,"score_spread":0.2560975672560755,"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."}}