{"id":"W2820099305","doi":"10.1109/ivcnz.2017.8402451","title":"Non-local pose means for denoising motion capture data","year":2017,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Electronic Arts (Canada)","funders":"","keywords":"Noise reduction; Computer vision; Artificial intelligence; Computer science; Noise (video); Motion capture; Motion field; Motion (physics); Virtual reality; Motion estimation; Gaussian noise; Image (mathematics)","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":[0.0009254034,0.0007749533,0.0007756766,0.0007366689,0.0003665759,0.000469785,0.0007915772,0.0008730149,0.001336125],"category_scores_gemma":[0.003532308,0.0003193814,0.0007388329,0.0009160784,0.0007139124,0.0008445422,0.0008775339,0.001345786,0.000799996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004127466,"about_ca_system_score_gemma":0.0006601007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001559157,"about_ca_topic_score_gemma":0.003123687,"domain_scores_codex":[0.9994413,0.0001532812,0.00002586198,0.0001401193,0.0002047363,0.00003473872],"domain_scores_gemma":[0.9993547,0.0002991262,0.00007354301,0.0001231285,0.0001273818,0.00002217846],"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":[0.0002848951,0.0001392066,0.001372058,0.0004783213,0.0001393176,0.0001510481,0.0003657298,0.1877627,0.08128886,0.03735891,0.006597564,0.6840613],"study_design_scores_gemma":[0.0000152696,0.0001255962,0.001592012,0.0000382294,0.00004175187,0.0001923279,0.00006350198,0.9389893,0.02579969,0.02277424,0.0103295,0.00003867543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004370139,0.0003346736,0.9945889,0.00009268879,0.00003660643,0.00001648416,0.00003388713,0.0001751846,0.000351503],"genre_scores_gemma":[0.2039467,0.001971625,0.7852751,0.000363091,0.0004041121,0.0002475089,0.0008433456,0.0003391042,0.006609362],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001559157,"threshold_uncertainty_score":0.004894078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07415122797980908,"score_gpt":0.3465566946479934,"score_spread":0.2724054666681843,"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."}}