{"id":"W979257376","doi":"10.1007/978-0-85729-997-0_29","title":"Multi-view 4D Reconstruction of Human Action for Entertainment Applications","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Research (Canada)","funders":"","keywords":"Computer science; Implementation; Visual hull; Entertainment; Action (physics); Pipeline (software); Action recognition; Artificial intelligence; Human–computer interaction; Computer graphics (images); Computer vision; Multimedia; Iterative reconstruction; Software engineering","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":[0.0002069872,0.0009157389,0.0005967452,0.0007250123,0.0001969032,0.00102887,0.0009185226,0.000809975,0.02070709],"category_scores_gemma":[0.000356748,0.0007843639,0.0008806335,0.0007872983,0.0003215398,0.0007443197,0.0007280973,0.0008675751,0.007169601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000337638,"about_ca_system_score_gemma":0.0004425044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00221043,"about_ca_topic_score_gemma":0.00519167,"domain_scores_codex":[0.9998549,0.00001469605,0.000005421798,0.00002448525,0.00008894529,0.00001159876],"domain_scores_gemma":[0.9999148,0.00002689366,0.000005251318,0.00002473686,0.00002183089,0.000006495665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001193733,0.00004386713,0.000315255,0.000474847,0.00009148773,0.000169149,0.0001547664,0.07249419,0.07087875,0.02342773,0.03283609,0.7989945],"study_design_scores_gemma":[0.00001969816,0.00007668877,0.001474205,0.0001672693,0.00006390279,0.001826129,0.0001006871,0.7774179,0.06424269,0.02291385,0.1316205,0.00007636859],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00200606,0.00148321,0.9841962,0.0001235664,0.0001116684,0.00003164262,0.0004366996,0.001461291,0.01014967],"genre_scores_gemma":[0.05053427,0.004988207,0.9003959,0.0001419016,0.000100298,0.00005897865,0.001881958,0.0008497593,0.04104882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02070709,"threshold_uncertainty_score":0.06927216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08928574589197365,"score_gpt":0.3377976425124268,"score_spread":0.2485118966204532,"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."}}