{"id":"W3204446819","doi":"10.1145/3479985","title":"Global Position Prediction for Interactive Motion Capture","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Computer Graphics and Interactive Techniques","topic":"Human Motion and Animation","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Innovationsfonden","keywords":"Computer science; Motion capture; Artificial intelligence; Orientation (vector space); Convolutional neural network; Position (finance); Motion (physics); Artificial neural network; Representation (politics); Displacement (psychology); Computer vision; Mathematics","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":[0.0002467694,0.001242607,0.000595696,0.0007807583,0.0002166345,0.0004486959,0.0009728489,0.0005469345,0.003727479],"category_scores_gemma":[0.00155834,0.0004430125,0.0004032274,0.0008408253,0.0002759471,0.0006725384,0.0007139063,0.0008307874,0.001118387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005123946,"about_ca_system_score_gemma":0.0004612216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01026416,"about_ca_topic_score_gemma":0.02187418,"domain_scores_codex":[0.999783,0.00002007861,0.000008293579,0.000090985,0.00006346049,0.00003428053],"domain_scores_gemma":[0.9996712,0.00009324856,0.00004103749,0.00009086046,0.00008022003,0.00002351881],"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":[0.0004203275,0.0001172192,0.004670685,0.0001355592,0.00009366262,0.0001358526,0.0000834071,0.2801755,0.03249708,0.002132838,0.01153482,0.6680031],"study_design_scores_gemma":[0.000007696775,0.00004545034,0.001946343,0.00001361256,0.0000113889,0.00006031744,0.00001285667,0.9850438,0.008368995,0.0015251,0.002954177,0.00001031688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03208121,0.0006058856,0.9561706,0.0001472016,0.0001142986,0.00007013755,0.001116385,0.006744414,0.002949926],"genre_scores_gemma":[0.6578795,0.0006498134,0.3269005,0.0002307386,0.0001478595,0.0001527522,0.005269052,0.0005701815,0.008199601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01026416,"threshold_uncertainty_score":0.02040881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00972562650812627,"score_gpt":0.2413963549396077,"score_spread":0.2316707284314814,"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."}}