Optimal Marker Set for Motion Capture of Dynamical Facial Expressions

We seek to determine an optimal set of markers for marker-based facial motion capture and animation control. The problem is addressed in two different ways: on the one hand, different sets of empirical markers classically used in computer animation are evaluated; on the other hand, a clustering method that automatically determines optimal marker sets is proposed and compared with the empirical marker sets. To evaluate the quality of a set of markers, we use a blendshape-based synthesis technique that learns the mapping between marker positions and blendshape weights, and we calculate the reconstruction error of various animated sequences created from the considered set of markers in comparison to ground truth data. Our results show that the clustering method outperforms the heuristic approach.