Adaptive Motion Data Representation with Repeated Motion Analysis

Abstract

In this paper, we present a representation method for motion capture data by exploiting the nearly repeated characteristics and spatiotemporal coherence in human motion. We extract similar motion clips of variable lengths or speed s across the database. Since the coding costs between these matched clips are small, we propose the repeated motion analysis to extract the referred and repeated clip pairs with maximum compression gains. For further utilization of motion coherence, we approximate the subspace - projected clip motions or residuals by interpolated functions with range - aware adaptive quantization. Our experiments demonstrate that the proposed feature - aware method is of high computational efficiency. Furthermore, it also provides substantial compression gain s with comparable reconstruction and perceptual errors.

Experiments

The proposed method used feature-aware clipping and curve fitting. It got satisfactory results in faithfulness. We use adaptive quantization and compact virtual markers with frame-based PCA. The PC axes overheads are much fewer than the clip-PCA method. In addition, the RMA can reduce the coefficient ranges of a repeated clip about two to sixteen times compared to primary ones. The adaptive quantization can further reduce the bit usages with few additional errors.

results of the proposed and comparative methods are shown below. Please refer to the manuscript and supplementary files for more results and comparison.

Table. User evaluation of the original motions and reconstructed motions of three methods.

Figure. Comparison of the original motion and decompressed motions by three methods. The original motions are shown in orange; the proposed results are shown in red; results of clip-based PCA [3] are shown in indigo; results of PGA-IK [13] are shown in black.

Publication

I-Chen Lin, Jen-Yu Peng, Chao-Chih Lin, Ming-Han Tsai, "Adaptive Motion Data Representation with Repeated Motion Analysis" IEEE Trans. Visualization and Computer Graphics, 17(4):527-538, April, 2011.

Paper: preprint_version (about 1.5MB), published version (link to the IEEE digital library)

BibTex

@article{LinTVCG10,
author = {I-Chen Lin, Jen-Yu Peng, Chao-Chih, Lin Ming-Han Tsai},
title = {Adaptive Motion Data Representation with Repeated Motion Analysis},
journal = {{IEEE} Transactions on Visualization and Computer Graphics},
volume = {17},
number = {4},
pages = {527--538},
month = {Apirl},
year = {2011},
doi = {10.1109/TVCG.2010.87}
}

Acknowledgements

The authors appreciate the helpful comments from the anonymous reviewers. They also thank volunteers participating in the user evaluation. Especially, they thank M.Tournier and other authors of PGA-IK method, who cordially provided their data for comparison. This paper was partially supported by the National Science Council, Taiwan under grant no. NSC 98-2221-E-009 -151 and 99-2221-E-009-136.