{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002234434,0.0007195069,0.0007841068,0.001068085,0.0003605894,0.001271529,0.001299212,0.001051101,0.004788889],"category_scores_gemma":[0.0009260384,0.000533039,0.0005714423,0.0008585909,0.0003516285,0.001254854,0.001987482,0.0006087019,0.001738427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002979465,"about_ca_system_score_gemma":0.0003837269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001556649,"about_ca_topic_score_gemma":0.002029856,"domain_scores_codex":[0.9994701,0.00004664539,0.00002092842,0.0001617819,0.0002314442,0.00006908227],"domain_scores_gemma":[0.9996256,0.0000477767,0.00004670442,0.0001895628,0.00005981401,0.00003051076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005117425,0.0001155778,0.004853169,0.0005646801,0.0001266318,0.001657547,0.0006281453,0.06275564,0.5585826,0.01096209,0.007518176,0.3517239],"study_design_scores_gemma":[0.00005029714,0.00033441,0.0252505,0.000206359,0.0001157615,0.006135949,0.0004472156,0.5046917,0.3958279,0.007721582,0.05903139,0.0001869893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07297688,0.001187865,0.9133936,0.0001313669,0.0001453431,0.0001178691,0.0007587427,0.00271433,0.008574059],"genre_scores_gemma":[0.6497335,0.001501355,0.3372298,0.0001775575,0.00008438846,0.0001303071,0.00181726,0.0004981832,0.00882758],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004788889,"threshold_uncertainty_score":0.01602036,"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."}}