Introducing semantic information into motion graph  

作　　者：刘渭滨 刘幸奇 邢薇薇 袁保宗 

机构地区：[1]Institute of Information Science,Beijing Jiaotong University [2]Beijing Key Laboratory of Advanced Information Science and Network Technology [3]School of Software Engineering,Beijing Jiaotong University

出　　处：《Journal of Central South University》2011年第4期1097-1104,共8页中南大学学报（英文版）

基　　金：Project(60801053) supported by the National Natural Science Foundation of China;Project(4082025) supported by the Beijing Natural Science Foundation,China;Project(20070004037) supported by the Doctoral Foundation of China;Project(2009JBM135,2011JBM023) supported by the Fundamental Research Funds for the Central Universities of China;Project(151139522) supported by the Hongguoyuan Innovative Talent Program of Beijing Jiaotong University,China;Project(YB20081000401) supported by the Beijing Excellent Doctoral Thesis Program,China;Project (2006CB303105) supported by the National Basic Research Program of China

摘　　要：To improve motion graph based motion synthesis,semantic control was introduced.Hybrid motion features including both numerical and user-defined semantic relational features were extracted to encode the characteristic aspects contained in the character's poses of the given motion sequences.Motion templates were then automatically derived from the training motions for capturing the spatio-temporal characteristics of an entire given class of semantically related motions.The data streams of motion documents were automatically annotated with semantic motion class labels by matching their respective motion class templates.Finally,the semantic control was introduced into motion graph based human motion synthesis.Experiments of motion synthesis demonstrate the effectiveness of the approach which enables users higher level of semantically intuitive control and high quality in human motion synthesis from motion capture database.To improve motion graph based motion synthesis, semantic control was introduced. Hybrid motion features including both numerical and user-defined semantic relational features were extracted to encode the characteristic aspects contained in the character＇s poses of the given motion sequences. Motion templates were then automatically derived from the training motions for capturing the spatio-temporal characteristics of an entire given class of semantically related motions. The data streams of motion documents were automatically annotated with semantic motion class labels by matching their respective motion class templates. Finally, the semantic control was introduced into motion graph based human motion synthesis. Experiments of motion synthesis demonstrate the effectiveness of the approach which enables users higher level of semantically intuitive control and high quality in human motion synthesis from motion capture database.

关 键 词：motion synthesis motion graph motion similarity semantic motion analysis motion annotation motion capture data 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]