Super-Resolved Facial Texture Under Changing Pose and Illumination
In this paper, we propose a method to incrementally super-resolve 3D facial texture by integrating information frame by frame from a video captured under changing poses and illuminations. First, we recover illumination, 3D motion and shape parameters from our tracking algorithm. This information is then used to super-resolve 3D texture using iterative back-projection (IBP) method. Finally, the super-resolved texture is fed back to the tracking part to improve the estimation of illumination and motion parameters. This closed-loop process continues to refine the texture as new frames come in. We also propose a local-region based scheme to handle non-rigidity of the human face. Experiments demonstrate that our framework not only incrementally super-resolves facial images, but recovers the detailed expression changes in high quality.