A hierarchical geostatistical model of walking style variety

This paper presents a new method on generating realistic human animation of various styles with given step constraints and a specific skeleton. Given a set of normal walking data captured from different subjects, a hierarchical geostatistical model is automatically learned to encode variety of walking styles by representing human body as a hierarchy of joint groups. For each child hierarchy level, there is a learned geostatistical model, whose low-dimensional control parameters are automatically constructed from the synthesized motion of its parent hierarchy level. The top level is controlled by the given step constraints. Also a realistic transition is finished during each three sequential stances from two interpolated cycles satisfying input step constraints thanks to the local model. We show that the normal walking styles are flexibly controlled by a simple graphlike user interface representing the given skeleton and steps. Our results demonstrate that this method would be helpful to remove the motion clones in group or crowd animation.