  Generating images with both photorealism and multiview 3D consistency is
crucial for 3D-aware GANs, yet existing methods struggle to achieve them
simultaneously. Improving the photorealism via CNN-based 2D super-resolution
can break the strict 3D consistency, while keeping the 3D consistency by
learning high-resolution 3D representations for direct rendering often
compromises image quality. In this paper, we propose a novel learning strategy,
namely 3D-to-2D imitation, which enables a 3D-aware GAN to generate
high-quality images while maintaining their strict 3D consistency, by letting
the images synthesized by the generator's 3D rendering branch to mimic those
generated by its 2D super-resolution branch. We also introduce 3D-aware
convolutions into the generator for better 3D representation learning, which
further improves the image generation quality. With the above strategies, our
method reaches FID scores of 5.4 and 4.3 on FFHQ and AFHQ-v2 Cats,
respectively, at 512x512 resolution, largely outperforming existing 3D-aware
GANs using direct 3D rendering and coming very close to the previous
state-of-the-art method that leverages 2D super-resolution. Project website:
https://seanchenxy.github.io/Mimic3DWeb.
