随着运动数据越来越多地被应用于动画制作和科研领域,高效的运动数据压缩技术也逐渐成为一个热门的研究课题。基于稀疏表达提出一种新的运动数据有损压缩方法。首先对输入的运动数据进行分析生成稀疏表达字典;然后基于稀疏表达字典对运动数据中的每一帧进行稀疏线性表达;最后用K-SVD算法对字典和稀疏表示进行迭代优化。实验结果表明,本文方法可以达到较高的压缩比(50倍左右),同时保持原始运动数据的完整性,还原后可控制重建误差在肉眼不易分辨的范围内(平均RMS误差2.0以下),并且本文方法特别适用于对较短运动数据的压缩。

As motion capture data is widely used nowadays, the compression of motion data becomes more and more important. In this paper, a sparse representation based approach is proposed for efficient compression of human motion data. A new algorithm is designed to extract the dictionary from an input motion clip automatically. Each frame of a motion clip can be represented by a sparse linear combination of the dictionary vectors. The experimental results show that our method can get a high compression ratio (about 50 times) for general short motion data, with a limited reconstruction error, which is hard to visually distinguish (ARMS error less than 2.0).