{"id":"W2008396997","doi":"10.1145/1477926.1477937","title":"Data-driven curvature for real-time line drawing of dynamic scenes","year":2009,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Curvature; Computer science; Animation; Computer graphics (images); Rendering (computer graphics); Artificial intelligence; Principal curvature; Skinning; Computer vision; Shape analysis (program analysis); Polygon mesh; Algorithm; Geometry; Mathematics; Mean curvature","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":[],"consensus_categories":[],"category_scores_codex":[0.0001519766,0.0001343315,0.0001806805,0.0002286883,0.0002039482,0.00004357756,0.001363046,0.00006723282,0.000005391654],"category_scores_gemma":[0.00004209384,0.0001264531,0.0001004338,0.0006263754,0.00004733844,0.0007006996,0.0000180474,0.0001925259,0.000003980244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001312845,"about_ca_system_score_gemma":0.00003968789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003515031,"about_ca_topic_score_gemma":0.00001125814,"domain_scores_codex":[0.9989563,0.00002445918,0.0002366415,0.0003869253,0.0001914653,0.0002042464],"domain_scores_gemma":[0.9980369,0.0001857439,0.00008831688,0.00151102,0.0001135117,0.00006450496],"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":[0.00005932674,0.0004951583,0.00002014935,0.00004695539,0.00006819617,0.00000490471,0.0003456704,0.004218597,0.03063004,0.008871524,0.0004257865,0.9548137],"study_design_scores_gemma":[0.0006184689,0.0003136901,0.0008211774,0.0001155345,0.00003865083,0.000009117914,0.00001872892,0.9678146,0.002545999,0.02415898,0.003284679,0.0002603346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001104524,0.00007063024,0.9949695,0.003196234,0.0001647484,0.0001702955,0.00009562165,0.0001671919,0.00006121485],"genre_scores_gemma":[0.4046998,0.000473888,0.5940216,0.0006215309,0.00001999664,0.000005307065,0.00004310728,0.000012703,0.000102115],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.963596,"threshold_uncertainty_score":0.5156608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248265027723869,"score_gpt":0.3300164162110197,"score_spread":0.297533765933781,"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."}}