Motion learning-based framework for unarticulated shape animation

Abstract

This paper presents a framework for generating animation sequences while maintaining desirable physical properties in a deformable shape. The framework consists of three important processes. Firstly, considering the given key pose configurations in the form of unarticulated meshes in high dimensional space, we cast our motion in low dimensional space using the unsupervised learning method of locally linear embedding (LLE). Corresponding to each point in LLE space, we can reconstruct the in-between pose using generalized radial basis functions. Next we create a map in the LLE space of the values for the different physical properties of the mesh, for example area, volume, etc. Finally, a probability distribution function in LLE space helps us rapidly choose the required number of in-between poses with desired physical properties. A significant advantage of this framework is that it relieves the animator the tedium of having to carefully provide key poses to suit the interpolant.