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中文摘要：
从运动捕获数据中提取出反映人体运动规律的基本动作单元，合成新的人体动画已成为研究热点．但已有动作单元提取方法忽略了运动序列的时序性和不同关节之间的运动相关性．针对该问题，提出了一种新的基本动作单元提取方法，首先，采用PCA方法对高维人体运动数据进行降维分析，并采用马氏距离平方度量姿态间的相似性；其次，结合动态时间归整方法和误差平方和准则对时序运动序列进行自动切分和标注；最后，建立不同动作单元之间的概率转移模型构建运动图，并根据约束条件合成新的逼真人体动画．
英文摘要：
Synthesizing high-quality human animations from the motion capture data is an important technology. The cost for the motion capture system is quite high, and the motion data cleaning is also an exhausting work. Usually, the existing motion capture data is a long motion sequence. Therefore, in many practical applications, it is difficult to create new animations from the long motion sequence directly. So it is a hot topic to extract the primitive movement from the existing motion capture data for synthesizing new animations. Many existing methods seldom consider the time sequence of motion data and the correlation among the joints. In this paper, a new technology is proposed to extract the primitive movement for synthesizing new animations. Firstly, PCA is adopted to map the high- dimensional motion data into a low-dimensional space, and the squared Mahalanohis distance is used to measure the similarity between different poses. Secondly, dynamic time warping and the sum of mean squared error are combined to segment and label the motion capture data automatically. Finally, a probability transfer model is proposed to construct the motion graph, which can be easily used for synthesizing new animations based on constraints.
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