基于非线性流形学习的3维人体运动合成

为了实现3维人体运动的有效合成，提出了一种基于非线性流形学习的3维人体运动合成框架及算法,并可应用于方便、快捷、用户可控的3维人体运动合成。该合成算法框架先采用非线性流形降维方法将高维运动样本映射到低维流形上，同时求解其本征运动语义参数空间的表达，然后将用户在低维运动语义参数空间中交互生成的样本通过逆向映射重建得到具有新运动语义特征的3维运动序列。实验结果表明该方法不仅能够对运动物理参数（如特定关节的运动位置、物理运动特征）进行较为精确的控制，还可用于合成具有高层运动语义（运动风格）的新运动数据。与现有运动合成方法比较，该方法具有用户可控、交互性强等优点，能够应用于常见3维人体运动数据的高效生成。

3D Human Motion Synthesis based on Nonlinear Manifold Learning

Due to the popularity of optical motion capture system, more realistic human motion data can be acquired easily and widely used in various applications such as video games, animation films, sports simulation and virtual reality. This paper proposes a framework and algorithm for 3D human motion synthesis based on nonlinear manifold learning. In this framework, high-dimensional motion samples are mapped into low-dimensional manifold, with nonlinear dimensionality reduction method, to the intrinsic representation of motion semantic features. Furthermore, the sample which is generated by user interactions in low-dimensional manifold can be reconstructed to obtain a 3D motion sequence which owns a new motion semantic feature by reverse mapping. The experimental results show that the method proposed in this paper can not only precisely control the physical features of motions(such as the location of a specific joint), but also can be used to synthesize new motion data which owns abstract motion semantic, such as motion styles.