Unsupervised learning of 3D human faces from unstructured 2D image data is an active research area. While recent works have achieved an impressive level of photorealism, they commonly lack control of lighting, which prevents the generated assets from being deployed in novel environments. To this end, we introduce LumiGAN, an unconditional Generative Adversarial Network (GAN) for 3D human faces with a physically based lighting module that enables relighting under novel illumination at inference time. Unlike prior work, LumiGAN can create realistic shadow effects using an efficient visibility formulation that is learned in a self-supervised manner. LumiGAN generates plausible physical properties for relightable faces, including surface normals, diffuse albedo, and specular tint without any ground truth data. In addition to relightability, we demonstrate significantly improved geometry generation compared to state-of-the-art non-relightable 3D GANs and notably better photorealism than existing relightable GANs.

该论文介绍了一个无条件的生成对抗网络LumiGAN，在物理上基于照明模块生成可以在推理时进行新颖照明的3D人脸。与以往的工作不同，LumiGAN可以在自我监督的学习中使用有效的可见性公式创建逼真的阴影效果。此外，作者还展示了LumiGAN与现有的3D GANs和可以进行新颖照明的GANs相比具有显着改进的几何生成和值得注意的更好逼真度。

LumiGAN: 无条件生成可再照明的3D人脸