Review for NeurIPS paper: GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

Weaknesses: The novelty is slightly limited, as this paper mostly feels like a combination of HoloGAN and NeRF. However, I think that this is not a deal-breaker, and I appreciate the authors' efforts to distill the experiments into key takeaways that can be relevant for future progress in 3D-aware generative modeling. I am surprised about the ray sampling used in the patch discriminator. Since the same discriminator views different decimated versions of the image, I am surprised that the effects of aliasing do not prevent learning high-frequency details (I would be less concerned if different discriminators were used for different levels of subsampling, but in the proposed method, the same discriminator sees differently aliased versions of the same content). Maybe this would be relevant when trying to scale up to even higher resolutions?