Geometry and texture estimation from a single face image is an ill-posed problem since there is very little information to work with. The problem further escalates when the face is rotated at a different angle. This paper tries to tackle this problem by introducing a novel method for texture estimation from a single image by first using StyleGAN and 3D Morphable Models. The method begins by generating multi-view faces using the latent space of GAN. Then 3DDFA trained on 3DMM estimates a 3D face mesh as well as a high-resolution texture map that is consistent with the estimated face shape. The result shows that the generated mesh is of high quality with near to accurate texture representation.

翻译：从单张人脸图像进行几何与纹理估计是一个病态问题，因为可供利用的信息极少。当人脸以不同角度旋转时，该问题会进一步加剧。本文通过提出一种基于StyleGAN与三维形变模型（3D Morphable Models）的单图像纹理估计新方法，尝试解决这一问题。该方法首先利用生成对抗网络（GAN）的潜在空间生成多视角人脸图像，随后通过基于3DMM训练的3DDFA（3D Dense Face Alignment）算法估算三维人脸网格及与估计人脸形状一致的高分辨率纹理贴图。实验结果表明，生成的网格质量较高，其纹理表征接近真实效果。