{"id":"W2612164513","doi":"","title":"Texture Transfer Based on Texture Descriptor Variations","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Texture filtering; Texture (cosmology); Texture compression; Projective texture mapping; Bidirectional texture function; Texture atlas; Artificial intelligence; Image texture; Computer science; Luminance; Displacement mapping; Computer vision; Texel; Pattern recognition (psychology); Orientation (vector space); Texture mapping; Mathematics; Image (mathematics); Geometry; Image segmentation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006879524,0.000576907,0.0007466126,0.0005776621,0.0001904696,0.0007635,0.0009883539,0.0007663972,0.002472006],"category_scores_gemma":[0.002072313,0.0002901409,0.0006644882,0.0004974474,0.001039882,0.00081204,0.00106159,0.0009364487,0.000685203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005926871,"about_ca_system_score_gemma":0.0002850308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007432027,"about_ca_topic_score_gemma":0.0005551266,"domain_scores_codex":[0.9996108,0.00007283754,0.00001338274,0.0001133924,0.0001427237,0.0000468349],"domain_scores_gemma":[0.9992633,0.0003558936,0.00008352278,0.000191082,0.00006626846,0.00003997832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002787643,0.00006904371,0.0006238368,0.0001422478,0.00006905174,0.0001748355,0.00007925939,0.710533,0.07566933,0.02039845,0.001324131,0.1906381],"study_design_scores_gemma":[0.00001233061,0.00005974952,0.0001889117,0.000006004241,0.00001326793,0.00008912949,0.000009098008,0.9796592,0.01185675,0.006926501,0.001170242,0.000008839157],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02694927,0.000282456,0.9698063,0.0001154957,0.00005748717,0.00004763591,0.00004451144,0.0005549699,0.002141861],"genre_scores_gemma":[0.8280819,0.0005025284,0.1624613,0.0002203501,0.00009713532,0.0001188797,0.0002013119,0.0002953447,0.008021189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002472006,"threshold_uncertainty_score":0.008269727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620584442376223,"score_gpt":0.2201415791930109,"score_spread":0.2039357347692487,"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."}}