Removal of 3D facial expressions: A learning-based approach

This paper focuses on the task of recovering the neutral 3D face of a person when given his/her 3D face model with facial expression. We propose a learning-based expression removal framework to tackle this task. Our basic idea is to model expression residue from samples, and then use the inferred expression residue from the input expressional face model to recover the neutral one. A two-step non-rigid alignment method is introduced to make all the face models topologically share a common structure. Then we construct two spaces, normal space and expression residue space, for modeling expression. Therefore, the expression removal problem can be formalized as the inference of expression residue from normal spaces. The neutral face model can be generated in a Poisson-based framework by the inferred expression residue. The experimental results on BU-3DFED database demonstrate the effectiveness of our approach.