Eurographics/acm Siggraph Symposium on Computer Animation (2006) Segment-based Human Motion Compression
Eurographics/acm Siggraph Symposium on Computer Animation (2006) Segment-based Human Motion Compression
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DOI：
10.2312/sca/sca06/127-135
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
M. Cani;J. O. O 'brien;Guodong Liu;L. McMillan
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文献类型：
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作者：
M. Cani;J. O. O 'brien;Guodong Liu;L. McMillan
随着越来越多的人体运动数据在许多应用中被广泛地用于动画计算机图形，对紧凑存储和快速传输的日益增长的需求使得对运动数据的压缩势在必行。我们提出了一种数据驱动的方法，通过利用数据的空间和时间相关性来有效地压缩人体运动序列。我们首先将运动序列分割成子序列，使得子序列中的姿势位于低维线性空间附近。然后，我们使用主成分分析对每个片段进行压缩。我们的方法通过只存储关键帧到主分量空间的投影，并通过样条函数插值其他帧来实现进一步的压缩。实验结果表明，该方法可以在较低的重建误差下获得较高的压缩比。
As more and more human motion data are becoming widely used to animate computer graphics figures in many applications, the growing need for compact storage and fast transmission makes it imperative to compress motion data. 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 principal component analysis. Our method achieves further compression by storing only the key frames' projections to the principal component space and interpolating the other frames in-between via spline functions. The experimental results show that our method can achieve significant compression rate with low reconstruction errors.