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

Pengjie Wang,Jiana Meng,Jiang Wang,Jing Xun,Xiaoming Wei

doi:10.1109/access.2020.2989430

Pengjie Wang, Jiana Meng + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.2989430

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Abstract

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.

Highlights

With the development of motion capture techniques, human motion data are widely used beyond the conventional fields of games and animation
Previous works [1]–[20] on motion compression have focused on two types of methods, namely, methods that reduce the redundancy of the time domain and those that reduce the redundancy of the space domain
WORK In this paper, we propose a compact representation for a motion database by employing effective recurring pattern discovery and analysis

Summary

Introduction

With the development of motion capture techniques, human motion data are widely used beyond the conventional fields of games and animation. These data are used in fields such as the automotive industry, arts, sports, virtual reality, and remote interaction in augmented reality. In a large motion database, there is a third way to reduce redundancy: the extraction of recurring similar motion patterns across a large database. These matched motion patterns can make coding more efficient. Few works have addressed motion compression with motion pattern discovery and analysis

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