  Recent years have seen remarkable progress in deep learning powered visual
content creation. This includes deep generative 3D-aware image synthesis, which
produces high-idelity images in a 3D-consistent manner while simultaneously
capturing compact surfaces of objects from pure image collections without the
need for any 3D supervision, thus bridging the gap between 2D imagery and 3D
reality. The ield of computer vision has been recently captivated by the task
of deep generative 3D-aware image synthesis, with hundreds of papers appearing
in top-tier journals and conferences over the past few years (mainly the past
two years), but there lacks a comprehensive survey of this remarkable and swift
progress. Our survey aims to introduce new researchers to this topic, provide a
useful reference for related works, and stimulate future research directions
through our discussion section. Apart from the presented papers, we aim to
constantly update the latest relevant papers along with corresponding
implementations at https://weihaox.github.io/3D-aware-Gen.
