{"id":"W3184641178","doi":"10.1109/iccv48922.2021.01101","title":"Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows","year":2021,"lang":"en","type":"preprint","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Bundesministerium für Bildung und Forschung; Ministry of Education; Deutsche Forschungsgemeinschaft","keywords":"Monocular; Benchmark (surveying); Generalization; Computer science; Probabilistic logic; Exploit; Artificial intelligence; Pose; Set (abstract data type); Posterior probability; Contrast (vision); Machine learning; Computer vision; Pattern recognition (psychology); Bayesian probability; Mathematics","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":[0.00249882,0.002440405,0.001927231,0.002209082,0.0006093351,0.001540157,0.003137727,0.001845048,0.002912156],"category_scores_gemma":[0.005608683,0.00138489,0.001669098,0.001828142,0.001706928,0.002338702,0.002952318,0.001816752,0.00174344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001552817,"about_ca_system_score_gemma":0.00219286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0118792,"about_ca_topic_score_gemma":0.01151851,"domain_scores_codex":[0.9982103,0.0004611242,0.00005636312,0.0006522324,0.0004808364,0.0001390599],"domain_scores_gemma":[0.9985606,0.0004800301,0.0001751135,0.0004265295,0.0002670638,0.00009060628],"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":[0.0003190376,0.0001167573,0.001181203,0.0001509234,0.0001036257,0.00009469085,0.0001355761,0.5174578,0.009216283,0.01415007,0.008914007,0.4481601],"study_design_scores_gemma":[0.00001660121,0.0000304162,0.0003630038,0.00001330202,0.000009266233,0.00009730281,0.00001185733,0.9828751,0.00357283,0.01134059,0.001651052,0.0000187064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005833404,0.0002115079,0.9900506,0.0001070984,0.00003154472,0.00007363685,0.0002128502,0.00271863,0.0007607137],"genre_scores_gemma":[0.1962565,0.0006396882,0.7957335,0.0003571619,0.0001730782,0.0003277813,0.002010176,0.0007861968,0.003715959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0118792,"threshold_uncertainty_score":0.02362013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04328864255393678,"score_gpt":0.3100232181090402,"score_spread":0.2667345755551034,"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."}}