Recent years have seen remarkable progress in deep learning powered visual
content creation. This includes 3D-aware generative image synthesis, which
produces high-fidelity 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 3D-aware generative models have shown that the introduction of 3D
information can lead to more controllable image generation. The task of
3D-aware image synthesis has taken the field of computer vision by storm, with
hundreds of papers accepted to top-tier journals and conferences in recent year
(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
this https URL.

介绍了近年来深度学习实现的视觉内容创作方法的显著进展，包括 3D 感知生成图像合成，旨在提供一个关于 3D 感知图像合成的综述，为相关领域的研究工作提供有用的参考，并通过我们的讨论部分激发未来的研究方向。