Local shape blending using coherent weighted regions

We present a novel local shape blending method that maps a sparse configuration of facial markers captured from an actor onto target models. The advantage of local shape blending methods is that, given a small set of key shapes for each local region, their combination can generate various facial expressions. However, they have the common problem that they use the pre-determined (fixed) regions and compute the combination of local key shapes for each region independently of each other. So, they have a risk of breaking natural correlations between the regions. We present a stochastic method of computing the regions and the blending weight vectors simultaneously. To do so, we formulate local shape blending as a problem of finding an optimal distribution of blending weight vectors of all control points in MAP–MRF framework.