{"id":"W3010312500","doi":"10.1371/journal.pone.0253157","title":"MoVi: A large multi-purpose human motion and video dataset","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; York University","funders":"Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Motion capture; Computer science; Artificial intelligence; Computer vision; Motion (physics); Kinematics; Computer graphics; Ground truth; Inertial measurement unit; Polygon mesh; Motion analysis; Pose; Computer graphics (images)","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":[0.0006912664,0.00253795,0.001247654,0.002708288,0.0007055831,0.0008632722,0.002292899,0.002466617,0.007542332],"category_scores_gemma":[0.002393618,0.0003984574,0.001215494,0.002269364,0.0006217086,0.0008200625,0.002104938,0.001282823,0.009406998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008371011,"about_ca_system_score_gemma":0.0009674739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01408672,"about_ca_topic_score_gemma":0.03899046,"domain_scores_codex":[0.999116,0.0001484881,0.00008673782,0.0002974537,0.0002269647,0.0001242788],"domain_scores_gemma":[0.9991597,0.000140237,0.00009282705,0.0002952908,0.0001777905,0.0001341122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001129613,0.0005724173,0.01562744,0.002625484,0.0004282827,0.0009785796,0.0003194527,0.005390672,0.008188392,0.001267254,0.8618967,0.1015757],"study_design_scores_gemma":[0.0007082814,0.000917573,0.2722108,0.001375495,0.0003463698,0.004583295,0.001413644,0.04554932,0.01288206,0.005189127,0.6543597,0.0004643945],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.035411,0.002530967,0.009163237,0.0006825211,0.0006433013,0.0005663908,0.9401621,0.006159937,0.004680636],"genre_scores_gemma":[0.02587819,0.0003497587,0.006728217,0.0001549773,0.00009625128,0.0004250128,0.9647812,0.0001334104,0.001453046],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01408672,"threshold_uncertainty_score":0.02800947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08260186657489497,"score_gpt":0.2716738108866361,"score_spread":0.1890719443117411,"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."}}