Scalable and Compact Representation for Motion Capture Data Using Tensor Decomposition

Junhui Hou, Lap-Pui Chau, Nadia Magnenat-Thalmann, Ying He

Abstract: Motion capture (mocap) technology is widely used in movie and game industries. Compact representation of the mocap data is critical to efficient storage and transmission. In this letter, we propose a novel tensor decomposition based scheme for compact and progressive representation of the mocap data. Our method segments and stacks the mocap sequence locally, and generates a 3rd-order tensor, which has strong correlation within and across slices of the tensor. Then, our method iteratively applies tensor decomposition in a multi-layer structure to explore the correlation characteristic. Experimental results demonstrate that the proposed scheme significantly outperforms existing algorithms in terms of scalability and storage requirement.