mlsanityData to 7 Oct 2026

A Survey on Deep Generative 3D-aware Image Synthesis

arXiv 2210.14267 ↗First posted 25 Oct 2022cs.CV cs.AI cs.GRcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 114 of its 155 references that are in the map. First posted on arXiv 25 Oct 2022.

Builds on

114 of 155 references.

References · 114

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38 more
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Citing papers per year: 2023: 3; 2024: 8; 2025: 4; 2026: 1.

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