Matching and blending human motions temporal scaleable dynamic programming
Matching and blending human motions temporal scaleable dynamic programming
复制标题
匹配和混合人体运动时间可缩放动态规划
DOI：
10.1109/iros.2004.1389366
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
K. Ikeuchi
中科院分区：
文献类型：
--
作者：
A. Nakazawa;Shin'ichiro Nakaoka;K. Ikeuchi
本文提出了一种对动作捕捉系统获取的人体动作帧进行匹配，然后根据匹配结果创建混合（插值）运动的方法。这种匹配方法基本上是动态规划（DP）匹配的一种变体，但我们对其进行了改进，使其能够检测时间尺度参数。这种可扩展动态规划（scalable - dp）可以匹配和评估同类运动，如步行、跑步、踏步及其时间尺度参数。这种方法适用于个体的差异，例如身体大小和停止帧的时间。在混合管道上，我们首先根据匹配结果生成关键帧。关键帧是通过考虑单个运动的时空差异来生成的。之后，在关键帧之间合成过渡运动。我们用15种步态动作和5种舞蹈动作来实验我们的方法。实验结果表明了该算法的有效性。
This paper presents a method for matching the frames of the human motions acquired by a motion capture system, and then creating blended (interpolated) motions according to the matching result. This matching method is basically a variation of a dynamic programming (DP) matching but we enhanced it to enable it to detect the timescale parameters. This scaleable dynamic programming (scaleable-DP) can match and evaluate the same class of motions such as walking, running, stepping and their timescale parameters. This approach is adaptable for differences in individuals, such as body sizes and timing of the stop-frames. On the blending pipeline, we first generate the keyframes according to the matching result. The keyframes are generated by considering the spatial and temporal difference of individual motions. After that, transition motions are synthesized between the keyframes. We experimented with our approach by using 15 gait motions and 5 dance motions. The results of these demonstrations show the validity of the proposed algorithm.