基于高斯过程动态模型的人体节奏运动合成

提出一种新的基于高斯过程动态模型的节奏转移方法。该方法能够准确、有效地将现有运动中的节奏信息转移到新的运动中去，适用于各种不同类型的运动。首先，使用短时（short term）PCA计算源运动的节奏点，组合3种重要的运动特征求解目标运动的特征点；然后，使用动态规划算法来找到两者之间的最佳匹配，最大化减少计算时间及对目标运动的修改；最后，使用高斯过程动态模型对目标运动进行学习，并在隐空间进行节奏化插值，最终合成新的节奏化运动。

Rhythmical motion synthesis based on Gaussian process dynamical model

In this paper, we provide a new rhythm translation method based on Gaussian process dynamical model which can translate existing rhythm information to various types of motion accurately and effectively. Firstly, Short-Term PCA is used to calculate rhythm points in source rhythmical motion, three important motion features are combined to extract the motion feature points in target motion; Secondly, dynamical programming is utilized to find the best match between them in order to reduce the estimation time and damage to the target motion; Lastly, the Gaussian process dynamical model is applied to learn the target motion and rhythmical interpolation is done in latent space to get the new final rhythmical motion.