In this paper, we propose a style translation filter that changes the attribute (style) of the motion coming from the actors' ages, genders, and so on. Using this filter, we can diversify the motions. Specifically, this filter is modeled by the Gaussian process regression that estimates the difference of pose (joint angles) between a neutral motion and the motion of a target attribute. In learning this filter, a key technique is to find pairs of corresponding posed from the sample motions. We solve this problem by employing the Multifactor Gaussian Process Model (MGPM) proposed by Wang et al. . In the experiments, we constructed multiple style translation filters from several attributes of walking motions, such as genders, ages, and emotions. The obtained filters were applied to some kinds of testing motions, such as walking, jumping, kicking, and dancing. The acquired motions were verified by a questionnaire study; the most of their attributes were changed to the filters' target attributes.