We propose a generative framework, FaceLit, capable of generating a 3D face
that can be rendered at various user-defined lighting conditions and views,
learned purely from 2D images in-the-wild without any manual annotation. Unlike
existing works that require careful capture setup or human labor, we rely on
off-the-shelf pose and illumination estimators. With these estimates, we
incorporate the Phong reflectance model in the neural volume rendering
framework. Our model learns to generate shape and material properties of a face
such that, when rendered according to the natural statistics of pose and
illumination, produces photorealistic face images with multiview 3D and
illumination consistency. Our method enables photorealistic generation of faces
with explicit illumination and view controls on multiple datasets - FFHQ,
MetFaces and CelebA-HQ. We show state-of-the-art photorealism among 3D aware
GANs on FFHQ dataset achieving an FID score of 3.5.

提出了一种生成框架 FaceLit，可从 2D 图像中学习纯 3D 面部形状和材质，并在各种用户定义的照明和视图条件下呈现，产生具有多视角和照明一致性的逼真人脸图像。