{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004088545,0.0001116821,0.0001058132,0.0002098201,0.0001186724,0.00004080884,0.0001622841,0.00007308125,0.0002399824],"category_scores_gemma":[0.000004588875,0.0001100733,0.0001256316,0.0003925348,0.00001754941,0.0001056982,0.00000118956,0.0002386446,0.0002132147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000137115,"about_ca_system_score_gemma":0.000003135661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006111094,"about_ca_topic_score_gemma":0.00001884799,"domain_scores_codex":[0.9994485,0.000007403443,0.0001355599,0.0001189688,0.0001075799,0.0001819683],"domain_scores_gemma":[0.9994832,0.00004529582,0.000008832944,0.000361995,0.00003491563,0.00006575719],"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":[0.000002073983,0.00007609973,0.0001956864,0.00004235513,0.0003304032,0.000002540041,0.00034123,0.8448864,0.003780629,0.00032084,0.001460263,0.1485615],"study_design_scores_gemma":[0.0003557062,0.00005500498,0.001693761,0.00005552001,0.0001551508,0.000009545772,0.0001884071,0.9758274,0.005058042,0.01438873,0.001624145,0.0005886147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1449257,0.00008907312,0.8528083,0.0003906485,0.0001383683,0.00006448727,0.000005989878,0.0005228541,0.001054624],"genre_scores_gemma":[0.9972441,0.000176198,0.002186571,0.0001923107,0.00002381413,0.00003614743,0.000003071435,0.00002741451,0.0001104127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8523184,"threshold_uncertainty_score":0.4488659,"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."}}