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

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

Blog Article

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 HERBAMARE VEG BROTH (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 Btowband Bridle 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.