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 ...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.展开更多
A novel method based on interval temporal syntactic model was proposed to recognize human activities in video flow. The method is composed of two parts: feature extract and activities recognition. Trajectory shape des...A novel method based on interval temporal syntactic model was proposed to recognize human activities in video flow. The method is composed of two parts: feature extract and activities recognition. Trajectory shape descriptor, speeded up robust features(SURF) and histograms of optical flow(HOF) were proposed to represent human activities, which provide more exhaustive information to describe human activities on shape, structure and motion. In the process of recognition, a probabilistic latent semantic analysis model(PLSA) was used to recognize sample activities at the first step. Then, an interval temporal syntactic model, which combines the syntactic model with the interval algebra to model the temporal dependencies of activities explicitly, was introduced to recognize the complex activities with a time relationship. Experiments results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for the recognition of complex activities.展开更多
基金Project(60801053) supported by the National Natural Science Foundation of ChinaProject(4082025) supported by the Beijing Natural Science Foundation,China+4 种基金Project(20070004037) supported by the Doctoral Foundation of ChinaProject(2009JBM135,2011JBM023) supported by the Fundamental Research Funds for the Central Universities of ChinaProject(151139522) supported by the Hongguoyuan Innovative Talent Program of Beijing Jiaotong University,ChinaProject(YB20081000401) supported by the Beijing Excellent Doctoral Thesis Program,ChinaProject (2006CB303105) supported by the National Basic Research Program of China
文摘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.
基金Project(50808025)supported by the National Natural Science Foundation of ChinaProject(20090162110057)supported by the Doctoral Fund of Ministry of Education,China
文摘A novel method based on interval temporal syntactic model was proposed to recognize human activities in video flow. The method is composed of two parts: feature extract and activities recognition. Trajectory shape descriptor, speeded up robust features(SURF) and histograms of optical flow(HOF) were proposed to represent human activities, which provide more exhaustive information to describe human activities on shape, structure and motion. In the process of recognition, a probabilistic latent semantic analysis model(PLSA) was used to recognize sample activities at the first step. Then, an interval temporal syntactic model, which combines the syntactic model with the interval algebra to model the temporal dependencies of activities explicitly, was introduced to recognize the complex activities with a time relationship. Experiments results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for the recognition of complex activities.
