Neural Facial Mesh Registration using Geometry Images and Teichmüller Refinement

Facial surface registration is an important research problem in computer vision and graphics, and has broad applications in the industries of gaming and movie. However, it is still challenging to accurately and efficiently register dense surfaces that undergo intense expression change. In this paper, we propose a novel registration method for facial meshes. By leveraging geometry images as the 3D representation, we reduce the 3D mesh registration problem to a 2D image registration problem. A Transformer-based neural network is trained to obtain a 2D deformation field and a Teichmüller map is calculated to smooth the deformation. During the inference stage, our method does not require human-prescribed landmarks and can explicitly output the correspondence between the source and the target faces. The experimental results show that our method surpasses compared methods in registration accuracy and achieves better smoothness of the induced mapping on the testing samples.