{"id":"W4386076278","doi":"10.1109/cvpr52729.2023.01207","title":"HumanGen: Generating Human Radiance Fields with Explicit Priors","year":2023,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Radiance; Prior probability; Computer science; Rendering (computer graphics); Generator (circuit theory); Artificial intelligence; Computer vision; Mesh generation; Representation (politics); Computer graphics (images); Finite element method","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":[0.00005950913,0.00009048602,0.0001040508,0.00008199466,0.000107663,0.0000364816,0.00008156768,0.00003922796,0.0001294944],"category_scores_gemma":[0.000002570435,0.00007579115,0.00003592704,0.0002646063,0.000005789688,0.00004894244,0.00001120449,0.00008540343,0.0001168434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001273084,"about_ca_system_score_gemma":0.000003609672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003390168,"about_ca_topic_score_gemma":0.0001817736,"domain_scores_codex":[0.9994875,0.000004674351,0.0001069549,0.0001223947,0.00009149744,0.0001870084],"domain_scores_gemma":[0.9997646,0.00001001697,0.000008480232,0.0001600213,0.00001329789,0.00004360202],"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":[3.875882e-7,0.000003085208,0.0004997987,0.00002430474,0.00005375262,0.00001301179,0.0005335551,0.984316,0.00604112,0.0002346274,0.005121809,0.003158548],"study_design_scores_gemma":[0.0001059685,0.00001595235,0.0001664352,0.00001795059,0.00001459893,0.000001058015,0.0001809622,0.9959757,0.002851498,0.00004068553,0.0004541065,0.0001750541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9314951,0.00009374105,0.05789252,0.00007384614,0.00004168966,0.00004140558,9.967173e-7,0.001047034,0.009313602],"genre_scores_gemma":[0.9942988,0.00002191322,0.001470395,0.00007130229,0.0001291104,0.00001499282,0.00001099455,0.0000249648,0.003957551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06280362,"threshold_uncertainty_score":0.3090673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831309204665187,"score_gpt":0.2239681183422669,"score_spread":0.2056550262956151,"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."}}