Xia, Weihao;
Xue, Jing-Hao;
(2022)
A Survey on Deep Generative 3D-aware Image Synthesis.
ACM Computing Surveys
, 56
(4)
, Article 90. 10.1145/3626193.
Preview |
PDF
3626193.pdf - Published Version Download (6MB) | Preview |
Abstract
Recent years have seen remarkable progress in deep learning powered visual content creation. This includes deep generative 3D-aware 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 field 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.
| Type: | Article |
|---|---|
| Title: | A Survey on Deep Generative 3D-aware Image Synthesis |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3626193 |
| Publisher version: | https://doi.org/10.1145/3626193 |
| Language: | English |
| Additional information: | © The Author(s), 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | deep generative models, generative adversarial network, implicit neural representation, diffusion probabilistic models, 3D-aware image synthesis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10181700 |
Archive Staff Only
 |
View Item |
