{"id":"W4312635677","doi":"10.1109/cvpr52688.2022.00509","title":"Generating Diverse and Natural 3D Human Motions from Text","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":485,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion (physics); Computer science; Representation (politics); Set (abstract data type); Artificial intelligence; Snippet; Sampling (signal processing); Computer vision; Space (punctuation); Text generation; Function (biology); Information retrieval; Programming language","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":[0.0004852159,0.001848814,0.000665788,0.001208008,0.0003849193,0.0005671968,0.001361943,0.001050409,0.00489062],"category_scores_gemma":[0.002869578,0.0004926487,0.001282467,0.0008604459,0.0006772327,0.0008187672,0.001103751,0.0007688917,0.001990877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005429998,"about_ca_system_score_gemma":0.0005106211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002900667,"about_ca_topic_score_gemma":0.007261894,"domain_scores_codex":[0.9995193,0.0001061936,0.0000267847,0.0001876212,0.0001270663,0.00003301884],"domain_scores_gemma":[0.9993594,0.0002814711,0.00005017958,0.000151669,0.0001040874,0.00005311623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001391684,0.0004663948,0.006181377,0.002280019,0.0002724827,0.002063703,0.0007784201,0.3649667,0.1210632,0.01042309,0.07983118,0.4102818],"study_design_scores_gemma":[0.000181934,0.0004161142,0.004465271,0.0001349037,0.00004976052,0.000917948,0.0003540536,0.9137675,0.04206752,0.008335453,0.02920641,0.000102997],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1269608,0.001301598,0.8274831,0.0004830717,0.0005280493,0.001164103,0.02090929,0.01420251,0.00696748],"genre_scores_gemma":[0.4378583,0.0009511039,0.4852461,0.0003798503,0.0001694433,0.00123631,0.06230418,0.001525316,0.01032931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00489062,"threshold_uncertainty_score":0.01636076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04597789898601981,"score_gpt":0.2749500740160118,"score_spread":0.228972175029992,"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."}}