UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A Survey on Deep Generative 3D-aware Image Synthesis

Xia, Weihao; Xue, Jing-Hao; (2022) A Survey on Deep Generative 3D-aware Image Synthesis. ArXiv: Ithaca, NY, USA. Green open access

[thumbnail of Xia_2210.14267.pdf]
Preview
Text
Xia_2210.14267.pdf

Download (9MB) | 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-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 this https URL [https://weihaox.github.io/3D-aware-Gen].

Type: Working / discussion paper
Title: A Survey on Deep Generative 3D-aware Image Synthesis
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.48550/arXiv.2210.14267
Language: English
Additional information: This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
Keywords: 3D-aware image synthesis, deep generative models, implicit neural representation, generative adversarial network, diffusion probabilistic models
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.ucl.ac.uk/id/eprint/10178016
Downloads since deposit
9Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item