{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000423005,0.0008541539,0.0004912649,0.0004077675,0.000216262,0.0007030563,0.001690406,0.000806389,0.006299894],"category_scores_gemma":[0.001556377,0.0004240818,0.0007174781,0.0004045464,0.0005600597,0.00127824,0.001093616,0.001342324,0.001614195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006004626,"about_ca_system_score_gemma":0.0005074692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005799375,"about_ca_topic_score_gemma":0.009357671,"domain_scores_codex":[0.9997664,0.00004459545,0.000006670949,0.0000855927,0.00007105886,0.00002566912],"domain_scores_gemma":[0.9997359,0.00008756299,0.00002954757,0.0000780924,0.00004414077,0.00002474581],"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":[0.0002341199,0.0001655949,0.0008706672,0.0002269445,0.00007592947,0.0001868462,0.0001234685,0.4625912,0.03463829,0.05926381,0.02629676,0.4153263],"study_design_scores_gemma":[0.00001249991,0.00002448377,0.0001822284,0.00001559065,0.000005226669,0.00005178431,0.000006882544,0.9736785,0.004170411,0.01641191,0.005426276,0.00001418549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004795114,0.0001607944,0.9879654,0.0001362129,0.00005642316,0.00002902386,0.0005111363,0.003610664,0.002735116],"genre_scores_gemma":[0.3445547,0.0005718022,0.6369806,0.000583685,0.0001318859,0.0002683421,0.002640878,0.001730546,0.01253753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006299894,"threshold_uncertainty_score":0.02107519,"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."}}