{"id":"W4226438175","doi":"10.48550/arxiv.2112.01983","title":"CoNeRF: Controllable Neural Radiance Fields","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Rendering (computer graphics); Artificial intelligence; Artificial neural network; Radiance; Computer vision; Deep neural networks","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001000438,0.0002063837,0.0002767854,0.0001309381,0.000154314,0.0002821157,0.0008359761,0.0002491233,0.000229555],"category_scores_gemma":[0.00002106316,0.0002525984,0.0001950796,0.0003018312,0.00004961195,0.0005079537,0.0006389174,0.0005215103,0.0001298343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008164551,"about_ca_system_score_gemma":0.0001609598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001536553,"about_ca_topic_score_gemma":0.00009616122,"domain_scores_codex":[0.9986089,0.0001171903,0.0001498056,0.0007850222,0.00006820663,0.0002709054],"domain_scores_gemma":[0.9987074,0.0000732288,0.0001578052,0.0007517679,0.0001737429,0.0001360531],"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":[0.000142783,0.0007079002,0.003877436,0.0004786794,0.000825262,0.007675008,0.001593461,0.7114702,0.0007691688,0.2327554,0.01341875,0.02628595],"study_design_scores_gemma":[0.001040192,0.00005100731,0.0007196038,0.00009774704,0.00006083498,0.00001730769,0.00007581613,0.9803578,0.0008320728,0.01367867,0.002487685,0.0005812717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5228195,0.0002028518,0.4653076,0.0004017268,0.001512664,0.0002141026,0.000009750256,0.0002794375,0.009252396],"genre_scores_gemma":[0.9949291,0.0001704648,0.0004317609,0.0006489596,0.0001262017,0.000001072323,0.00002433399,0.000009004021,0.003659054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4721097,"threshold_uncertainty_score":0.9999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0669905802813057,"score_gpt":0.1765483698066952,"score_spread":0.1095577895253895,"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."}}