{"id":"W6912775799","doi":"10.5281/zenodo.7525412","title":"3D-Aware Semantic-Guided Generative Model for Human Synthesis","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Parks and Wilderness Society","funders":"Horizon 2020 Framework Programme","keywords":"Generative grammar; Set (abstract data type); Field (mathematics); Texture synthesis; Generative model; Code (set theory); Computer graphics; Image synthesis","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":[0.0003538236,0.0009473496,0.0005564439,0.0004314197,0.0001790898,0.0005717719,0.0009725347,0.0009379878,0.004063872],"category_scores_gemma":[0.0008582658,0.0005226825,0.001176683,0.0003335559,0.0007133207,0.0005298094,0.0009826476,0.001233442,0.001453401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007327079,"about_ca_system_score_gemma":0.0004969325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002922366,"about_ca_topic_score_gemma":0.004780045,"domain_scores_codex":[0.9998229,0.00004390688,0.000005035925,0.00006004668,0.00004856634,0.00001968892],"domain_scores_gemma":[0.9998198,0.00009655418,0.00001584047,0.00003664567,0.0000176839,0.00001353729],"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":[0.00008722568,0.00002947467,0.000287627,0.00006798198,0.0000504552,0.00009248326,0.00004821067,0.9060994,0.00964099,0.0135892,0.004324863,0.06568215],"study_design_scores_gemma":[0.000006857876,0.000009465466,0.00004218257,0.00000562651,0.000005275907,0.00004265234,0.000002995904,0.9913791,0.001486227,0.005633939,0.001380935,0.000004608026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006442756,0.0004410662,0.9881763,0.0002006575,0.00006335929,0.0000285622,0.0002759235,0.001518228,0.002853199],"genre_scores_gemma":[0.5841333,0.0009823268,0.3926898,0.0007612411,0.0001290859,0.0002829109,0.002040569,0.001173388,0.0178073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004063872,"threshold_uncertainty_score":0.01359499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06255316187991244,"score_gpt":0.2628244228606647,"score_spread":0.2002712609807523,"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."}}