Motion synthesis for affective agents using piecewise principal component regression
Motion synthesis for affective agents using piecewise principal component regression
复制标题
使用分段主成分回归进行情感主体的运动合成
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
S. Sakazawa
中科院分区：
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作者：
Jianfeng Xu;E. Myodo;S. Sakazawa
人类运动的情感风格对于使用具体会话代理的人机交互至关重要。情感主体的运动合成从输入中性运动生成特定情感风格的骨骼运动（在本文中简称为情感运动）。这对用户很有吸引力，但由于众所周知的事实，即骨骼运动是高维和非线性信号，因此非常具有挑战性。我们通过使用回归分析来估计中性运动和情感运动之间的关系来解决这个问题，并首次采用主成分回归（PCR）来处理高维运动信号。此外，我们提出了一种称为分段主成分回归（PPCR）的新方法来处理非线性问题，其中运动信号被自动分为几个片段，并对每个片段执行 PCR。我们的实验结果表明，所提出的 PPCR 方法成功地产生了高质量的情感运动。
An affective style of human motion is essential for human computer interaction using embodied conversational agents. Motion synthesis for affective agents generates a skeletal motion in a particular affective style (briefly called affective motion in this paper) from an input neutral motion. This appeals to the user but is very challenging due to the well-known fact that a skeletal motion is a high-dimensional and non-linear signal. We solve this problem by using regression analysis to estimate the relationship between neutral motions and affective motions, adopting principal component regression (PCR) to deal with the high-dimensional motion signal for the first time. Furthermore, we propose a novel method called piecewise principal component regression (PPCR) to deal with the non-linear problem, in which the motion signal is automatically divided into several segments and PCR is performed on each segment. Our experimental results demonstrate that the proposed PPCR method is successful in generating affective motion within high quality.