Estimating coloured 3d face models from single images: An example based approach

Abstract

In this paper we present a method to derive 3D shape and surface texture of a human face from a single image. The method draws on a general flexible 3D face model which is "learned" from examples of individual 3D-face data (Cyberware-scans). In an analysis-by-synthesis loop, the flexible model is matched to the novel face image. From the coloured 3D model obtained by this procedure, we can generate new images of the face across changes in viewpoint and illumination. Moreover, nonrigid transformations which are represented within the flexible model can be applied, for example changes in facial expression. The key problem for generating a flexible face model is the computation of dense correspondence between all given 3D example faces. A new correspondence algorithm is described which is a generalization of common algorithms for optic flow computation to 3D-face data.