{"id":"W3214483306","doi":"10.1109/wacvw54805.2022.00077","title":"Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Rendering (computer graphics); Motion blur; Pixel; View synthesis; Depth of field; Image (mathematics); Computer graphics (images)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000593758,0.0004282524,0.000500952,0.0002832156,0.0003361411,0.000339415,0.001462439,0.0001252822,0.0008497504],"category_scores_gemma":[0.0001586486,0.0004126663,0.0002389419,0.0004500605,0.00003895399,0.0005036195,0.004867544,0.0008449422,0.0002301233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000210996,"about_ca_system_score_gemma":0.00009043507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001111929,"about_ca_topic_score_gemma":0.000004535545,"domain_scores_codex":[0.9968588,0.0002477521,0.0005735725,0.001282314,0.0005748851,0.0004626664],"domain_scores_gemma":[0.9977637,0.0001837195,0.0003368121,0.00142975,0.0001209785,0.0001650434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000322669,0.0001684875,0.00008964647,0.0001557996,0.00004810202,0.00004082398,0.0004636905,0.008458375,0.003599673,0.0006229145,0.0002618355,0.9860874],"study_design_scores_gemma":[0.0002003886,0.00001085235,0.000646947,0.0001615481,0.00001880881,0.00002461785,0.00006783565,0.9851749,0.001958964,0.001836672,0.009305901,0.0005925716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003081812,0.0004380633,0.9939042,0.0006374097,0.001178231,0.0008214561,0.000007692557,0.0009509173,0.001753834],"genre_scores_gemma":[0.09792317,0.000346784,0.8967163,0.001073535,0.00009475211,0.001006692,0.00002898527,0.00006670378,0.002743084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9854949,"threshold_uncertainty_score":0.9998325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04128202683622821,"score_gpt":0.3108655144665973,"score_spread":0.2695834876303691,"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."}}