CMU Thesis Proposal : Face View Synthesis Using A Single Image (3 May 2006)

Jiang Ni
Robotics Institute
Carnegie Mellon University

Abstract
Face view synthesis involves using one view of a face to artificially render another view. It is an interesting problem in computer vision and computer graphics, and can be applied in the entertainment industry such as animated movies or video games. The fact that the input is only a single image, makes the problem very difficult. Previous approaches perform machine learning on pair of poses from 2D training data and then predict the unknown pose in the test example. Such 2D approaches are much more practical than approaches requiring 3D data and more computationally efficient. However they perform inadequately when dealing with large angles between poses. In this proposal we seek to improve performance through better choices in probabilistic modeling. As a first step, we have implemented a statistical model combining distance in feature space (DIFS) and distance from feature space (DFFS) for such pair of poses. Such a representation leads to better performance. Furthermore, we have observed that statistical dependency varies among different groupings of pixels. In particular, a given pixel variable is often statistically correlated with only a small number of other pixel variables. We propose to exploit this statistical structuring by modeling the synthesis problem using graphical probability models. Such representations concisely describe the synthesis problem, providing a rich model with reduced susceptibility to over-fitting.

More detail : http://www.cs.cmu.edu/~jiangni/thesis/jiang_proposal.pdf