As more and more human motion data are widely used to animate computer graphics figures in many applications, there is an imperative need to compress motion data for compact storage and fast transmission. We propose a data-driven method for efficient compression of human motion sequences by exploiting both spatial and temporal coherences of the data. We first segment a motion sequence into subsequences such that the poses within a subsequence lie near a low dimensional linear space. We then compress each segment using the principal component analysis. Further compression is achieved by storing only the key frames? projections to the principal component space and interpolating the other frames in-between the key frames via spline functions. The experimental results show that our method can achieve significant compression rate with low reconstruction errors.