Real-time data driven deformation with affine bones

Data driven deformation is increasingly important in computer graphics and interactive applications. From given mesh example sequences, we train a deformation predictor and manipulate a specific style of surface deformation interactively using only a small number of control points. The latest approach of learning the connection between rigid bone transformations and control points uses a statistically based framework, called canonical correlation analysis. In this paper, we extend this approach to a skinned mesh with affine bones, each of which conveys a nonrigid affine transformation. However, it is difficult to discover the underlying relationship between control points and nonrigid transformations. To address this issue, we present a two-layer regression framework; one layer being from control points to rigid and the other layer being from rigid to nonrigid transformations. Our contributions also include bone-vertex weight smoothing, enabling the distribution of each bone’s influence across neighboring vertices. We can alleviate distortion around regions where nearby bones undergo various transformations and improve deformations reaching beyond the learned subspaces. Experimental results show that our method can achieve more general deformations including flexible muscle bulges or twists. The performance of our implementation is comparable to the latest approach.