{"id":"W2071661704","doi":"10.1145/2461912.2461960","title":"Implicit skinning","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; Networks of Centres of Excellence of Canada; Agence Nationale de la Recherche; Royal Society; Intel Corporation","keywords":"Skinning; Computer science; Animation; Computer graphics (images); Pipeline (software); Set (abstract data type); Position (finance); Triangle mesh; Motion capture; Computation; Volume (thermodynamics); Algorithm; Computer vision; Polygon mesh; Motion (physics); Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004813512,0.001041542,0.0007871393,0.0007297578,0.0004000798,0.001272264,0.001669268,0.001054372,0.01468813],"category_scores_gemma":[0.002113556,0.0005650484,0.0008749882,0.000586072,0.0007815422,0.001443118,0.002257569,0.001355583,0.004087879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003047216,"about_ca_system_score_gemma":0.0005293251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009766564,"about_ca_topic_score_gemma":0.001317319,"domain_scores_codex":[0.9993424,0.00008303148,0.00003096396,0.0001448308,0.0003315377,0.00006722742],"domain_scores_gemma":[0.9994492,0.0001483619,0.00003851033,0.0002166598,0.0001138872,0.00003338737],"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":[0.0002232645,0.00007692788,0.0006434662,0.0004418882,0.00008309451,0.000280182,0.0003906463,0.2541015,0.06599973,0.07039747,0.01171227,0.5956495],"study_design_scores_gemma":[0.00003286692,0.00007277872,0.0002295532,0.00004273062,0.00002481904,0.0003827732,0.00007253019,0.8828419,0.03565739,0.02497484,0.05563171,0.0000361833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003214178,0.0001350881,0.990001,0.00003229874,0.00009041523,0.00004470722,0.0000514595,0.001493915,0.004936942],"genre_scores_gemma":[0.2199238,0.0005794037,0.7514353,0.000193121,0.00009065491,0.0001674265,0.0005909177,0.00240834,0.0246109],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01468813,"threshold_uncertainty_score":0.0491367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224152012269939,"score_gpt":0.2141762399644934,"score_spread":0.201934719841794,"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."}}