  We have witnessed rapid progress on 3D-aware image synthesis, leveraging
recent advances in generative visual models and neural rendering. Existing
approaches however fall short in two ways: first, they may lack an underlying
3D representation or rely on view-inconsistent rendering, hence synthesizing
images that are not multi-view consistent; second, they often depend upon
representation network architectures that are not expressive enough, and their
results thus lack in image quality. We propose a novel generative model, named
Periodic Implicit Generative Adversarial Networks ($\pi$-GAN or pi-GAN), for
high-quality 3D-aware image synthesis. $\pi$-GAN leverages neural
representations with periodic activation functions and volumetric rendering to
represent scenes as view-consistent 3D representations with fine detail. The
proposed approach obtains state-of-the-art results for 3D-aware image synthesis
with multiple real and synthetic datasets.
