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 https://weihaox.github.io/awesome-3D-aware-synthesis.
翻译:近些年来,在深层学习有动力的视觉内容创建方面取得了显著进展,其中包括3D-觉醒图像合成,以三维一致的方式生成高贞洁图像,同时从纯图像收藏中捕获物体的紧凑面,而无需任何三维监督,从而缩小了2D图像与三维现实之间的差距。3D-觉醒的基因化模型表明,引入3D信息可以导致更可控的图像生成。3D-觉醒图像合成的任务通过风暴将计算机视觉领域引入了风暴,近年顶级期刊和会议(主要是过去两年)接受了数百篇论文,但缺乏对这一显著和快速进展的全面调查。我们的调查旨在引入新的研究人员,为相关作品提供有用的参考,并通过我们的讨论部分激励今后的研究方向。除了提供的文件外,我们的目标是不断更新最新的相关文件,同时在 https://weihaox.github.io/aweome-3D-aware-synsis进行相应的实施。