Motion synthesis for affective agents using piecewise principal component regression

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.