{"id":"W2082145490","doi":"10.1145/1360612.1360698","title":"Markerless garment capture","year":2008,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":181,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Motion capture; Computer science; Computer graphics (images); Computer vision; Motion (physics); Artificial intelligence; Range (aeronautics); Clothing; Geometry; Mathematics; Engineering","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":[0.00004693696,0.0001266131,0.0001193988,0.0001805166,0.0001779025,0.000009913882,0.0001546223,0.00008792683,0.00009632956],"category_scores_gemma":[0.000002866921,0.0001244234,0.0001524303,0.0003509063,0.0000336353,0.00004734455,0.00000105448,0.0002714031,0.00003805927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002185527,"about_ca_system_score_gemma":0.000007362239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001934519,"about_ca_topic_score_gemma":0.00003558832,"domain_scores_codex":[0.9993939,0.00001189155,0.0001320198,0.000132615,0.0001613246,0.0001682463],"domain_scores_gemma":[0.9994884,0.00003293205,0.000008992703,0.0003725624,0.0000273347,0.00006985082],"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":[0.00001662112,0.0001859804,0.0003718733,0.00005508418,0.0004151196,0.00004666242,0.0009769528,0.9780805,0.0003544754,0.00009280917,0.002648422,0.01675552],"study_design_scores_gemma":[0.001701577,0.0001147403,0.004166767,0.0001518121,0.0005812066,0.0001846626,0.0006633821,0.9667429,0.005068079,0.003462394,0.01535644,0.00180608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1520602,0.0003524254,0.8449563,0.0003677315,0.0002766918,0.00006719347,0.0000263461,0.0005258668,0.001367202],"genre_scores_gemma":[0.9967617,0.001403516,0.001266984,0.0001552737,0.00002116885,0.00001590062,0.000006030521,0.00002593602,0.0003435233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8447014,"threshold_uncertainty_score":0.5073838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925767049380122,"score_gpt":0.2077625957374273,"score_spread":0.188504925243626,"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."}}