We propose a framework, called LiftedGAN, that disentangles and lifts a
pre-trained StyleGAN2 for 3D-aware face generation. Our model is "3D-aware" in
the sense that it is able to (1) disentangle the latent space of StyleGAN2 into
texture, shape, viewpoint, lighting and (2) generate 3D components for
rendering synthetic images. Unlike most previous methods, our method is
completely self-supervised, i.e. it neither requires any manual annotation nor
3DMM model for training. Instead, it learns to generate images as well as their
3D components by distilling the prior knowledge in StyleGAN2 with a
differentiable renderer. The proposed model is able to output both the 3D shape
and texture, allowing explicit pose and lighting control over generated images.
Qualitative and quantitative results show the superiority of our approach over
existing methods on 3D-controllable GANs in content controllability while
generating realistic high quality images.

提出 LiftedGAN 框架，无需手动标注或 3DMM 模型的训练，通过可微分的渲染器分解 StyleGAN2 的潜在空间，生成 3D 组件，并输出 3D 形状和纹理，实现内容可控性，在生成逼真高质量图像方面表现出超越现有方法的优点。