{"id":"W4353111635","doi":"10.48550/arxiv.2303.12074","title":"CC3D: Layout-Conditioned Generation of Compositional 3D Scenes","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Compute Canada; Samsung; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Computer science; Representation (politics); Artificial intelligence; Process (computing); Generative grammar; Focus (optics); Generative model; Computer vision; 3d model; Quality (philosophy); Field (mathematics); Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004428204,0.0009561836,0.0004528517,0.0003473991,0.0002170468,0.0007284748,0.001115273,0.0007551001,0.004500172],"category_scores_gemma":[0.001239564,0.0005053715,0.0009302199,0.0002982788,0.0007016971,0.0006531877,0.001184453,0.001348387,0.001399448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006211954,"about_ca_system_score_gemma":0.0005405933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002248833,"about_ca_topic_score_gemma":0.005323285,"domain_scores_codex":[0.9997742,0.00006021625,0.000005424302,0.00006528002,0.0000733148,0.00002159117],"domain_scores_gemma":[0.9996462,0.0001713836,0.0000185131,0.00009174404,0.00003919788,0.00003299212],"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":[0.0001323066,0.00008140237,0.001117467,0.0002051802,0.00009154994,0.0002943475,0.0001401966,0.8073471,0.03711068,0.03511569,0.01635173,0.1020123],"study_design_scores_gemma":[0.00001605697,0.0000220534,0.0001084609,0.00001015864,0.000006625119,0.00009687436,0.000008636105,0.982025,0.004720285,0.008670267,0.004305226,0.0000103073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006206693,0.0001860676,0.9872387,0.0001020967,0.0000711647,0.00005298007,0.0003544397,0.00298819,0.002799663],"genre_scores_gemma":[0.3967169,0.0004358463,0.5889764,0.0005821606,0.00008503336,0.0003193829,0.002914346,0.002941601,0.007028362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004500172,"threshold_uncertainty_score":0.01505458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1724681261143324,"score_gpt":0.2291818422242067,"score_spread":0.05671371610987436,"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."}}