PGAN: Prediction Generative Adversarial Nets for Meshes

Abstract

Unlike images, the topology similarity among meshes can hardly be handled with traditional signal processing tools because of their irregular structures. Geometry image parameterization provides a way to represent 3D meshes in the form of 2D geometry and normal images. However, most existing methods, including the CoGAN are not suitable for such unnatural images corresponding to meshes. To solve this problem, we propose a Prediction Generative Adversarial Network (PGAN) to learn a joint distribution of geometry and normal images for generating meshes. Particularly, we enforce a prediction constraint on the geometry GAN and normal GAN in our PGAN utilizing the inherent relationship between the geometry and normal. The experimental results on face mesh generation indicate that our PGAN outperforms in generating realistic face models with rich facial attributes such as facial expression and retaining the geometry of the faces.