{"id":"W3215649702","doi":"10.1016/j.patcog.2021.108439","title":"3D pose estimation and future motion prediction from 2D images","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Alberta","funders":"","keywords":"Benchmark (surveying); Computer science; Kinematics; Pose; Artificial intelligence; Encoder; Motion capture; Task (project management); Projection (relational algebra); Sequence (biology); Motion (physics); Motion estimation; Representation (politics); RGB color model; Computer vision; Encoding (memory); Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001212942,0.0001465658,0.0001238516,0.0001111866,0.0001862771,0.0003120802,0.00008638678,0.000115218,0.0003847636],"category_scores_gemma":[0.00002483583,0.0001589415,0.00004798079,0.0001843468,0.0000188102,0.0013744,0.00006112763,0.00015729,0.0003088002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004042089,"about_ca_system_score_gemma":0.00002158046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003611634,"about_ca_topic_score_gemma":0.00001698414,"domain_scores_codex":[0.9987774,0.0001289638,0.0002454203,0.0004619324,0.0002243462,0.0001618837],"domain_scores_gemma":[0.9993214,0.00005253593,0.0001209471,0.0002196675,0.0002056123,0.00007984311],"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":[0.000003391839,0.00006428341,0.0005963005,0.00002168134,0.00001612522,0.0000161926,0.0002395779,0.000004076302,0.002364235,0.00001213041,0.0003740485,0.9962879],"study_design_scores_gemma":[0.003826736,0.0003185459,0.3452089,0.0006381094,0.0002733418,0.0005352999,0.000717524,0.3649632,0.2122237,0.06453823,0.005310788,0.001445643],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2661035,0.0001743667,0.7296509,0.001235144,0.00124153,0.0001473909,0.0001731279,0.0002914388,0.00098265],"genre_scores_gemma":[0.9693431,0.0005492075,0.02500622,0.0009482101,0.001598605,0.00005178048,0.002415705,0.00002012028,0.00006700544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9948423,"threshold_uncertainty_score":0.6481448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01518293046901832,"score_gpt":0.2254038673814595,"score_spread":0.2102209369124412,"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."}}