Generating realistic 3D faces is of high importance for computer graphics and
computer vision applications. Generally, research on 3D face generation
revolves around linear statistical models of the facial surface. Nevertheless,
these models cannot represent faithfully either the facial texture or the
normals of the face, which are very crucial for photo-realistic face synthesis.
Recently, it was demonstrated that Generative Adversarial Networks (GANs) can
be used for generating high-quality textures of faces. Nevertheless, the
generation process either omits the geometry and normals, or independent
processes are used to produce 3D shape information. In this paper, we present
the first methodology that generates high-quality texture, shape, and normals
jointly, which can be used for photo-realistic synthesis. To do so, we propose
a novel GAN that can generate data from different modalities while exploiting
their correlations. Furthermore, we demonstrate how we can condition the
generation on the expression and create faces with various facial expressions.
The qualitative results shown in this paper are compressed due to size
limitations, full-resolution results and the accompanying video can be found in
the supplementary documents. The code and models are available at the project
page: this https URL

本研究提出了一种新颖的 GAN 方法，可以联合生成面部纹理、形状和法向量信息，用于进行类真实面部合成。同时，我们展示了如何以面部表情为条件生成具有不同表情的面部。