{"id":"W2375068441","doi":"","title":"The Research and Realization In Hand Language Animation Of 3D Virtual Human Based On OpenGL","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Animation; Key frame; Gesture; OpenGL; Computer facial animation; Computer animation; Key (lock); Frame (networking); Motion (physics); Artificial intelligence; Computer graphics (images); Computer vision; Virtual actor; Facial motion capture; Skeletal animation; Realization (probability); Virtual reality; Visualization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004738233,0.0004576279,0.0003509516,0.0005063721,0.0003396921,0.0008001731,0.0009008222,0.0005161889,0.003301126],"category_scores_gemma":[0.00097408,0.0003206594,0.0006648058,0.0003399583,0.0006908192,0.001713768,0.0006801001,0.0006606539,0.0004707826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003066804,"about_ca_system_score_gemma":0.0005825649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002132629,"about_ca_topic_score_gemma":0.001372696,"domain_scores_codex":[0.9994235,0.0001073546,0.00002812705,0.00009132176,0.0002999621,0.00004968493],"domain_scores_gemma":[0.99971,0.0001053036,0.00001678937,0.00005912971,0.00008169445,0.00002696038],"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":[0.0003388991,0.00006458678,0.002215218,0.000658826,0.00007303756,0.000654402,0.002040119,0.04462975,0.2679299,0.1100879,0.003907537,0.5673999],"study_design_scores_gemma":[0.0001180817,0.0006081088,0.002864385,0.0001596729,0.0001686718,0.002558729,0.0004673894,0.6312376,0.2121597,0.04133116,0.1081502,0.0001763272],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01500797,0.0005653405,0.9762599,0.0001180481,0.00006234407,0.0000272802,0.00002308613,0.001695612,0.006240452],"genre_scores_gemma":[0.4502892,0.001546845,0.5384966,0.0001366101,0.00007163064,0.0001118432,0.0001963148,0.0003940117,0.008756923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003301126,"threshold_uncertainty_score":0.01104331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06122790399235081,"score_gpt":0.3264602148135867,"score_spread":0.2652323108212359,"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."}}