Motion Recurring Pattern Analysis: A Lossless Representation for Motion Capture Databases

In this paper, we propose the motion recurring pattern analysis (MRPA) method for the lossless representation of a motion database at the segment level instead of the motion degree of freedom (DOF) level. First, we concatenate all the motions into a long sequence in the motion database, and we discover similar posture paths by building a matching trellis structure based on the randomized k-d tree. Second, horizontal segments of paths are suitably refined, based on a self-organizing map, to obtain the optimized segmentation for maximum compression gains. Third, by using the path as a connection agent, these segments are clustered into a forest of trees. With this forest structure, we obtain the prediction residuals (the differences between the nonroot branches and their parents), and the differences between neighboring residuals are encoded under floating-point compression. Relative to previous lossless compression methods, our approach can achieve a higher compression ratio with comparable decompression time costs.