Abstract
With the development of Motion capture techniques, more and more 3D motion libraries become available. In this paper, we present a novel content-based 3D motion retrieval algorithm. We partition the motion library and construct a motion index tree based on a hierarchical motion description. The motion index tree serves as a classifier to determine the sub-library that contains the promising similar motions to the query sample. The Nearest Neighbor rule-based dynamic clustering algorithm is adopted to partition the library and construct the motion index tree. The similarity between the sample and the motion in the sub-library is calculated through elastic match. To improve the efficiency of the similarity calculation, an adaptive clustering-based key-frame extraction algorithm is adopted. The experiment demonstrates the effectiveness of this algorithm.
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