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Review Article | Open Access

VR content creation and exploration with deep learning: A survey

State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China.
Peng Cheng Laboratory, Shenzhen 518000, China.
School of Engineering and Computer Science, Victoria University of Wellington, New Zealand.
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Abstract

Virtual reality (VR) offers an artificial, com-puter generated simulation of a real life environment. It originated in the 1960s and has evolved to provide increasing immersion, interactivity, imagination, and intelligence. Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion, they can build very powerful models which leverage large quantities of visual media data. Intelligence of VR methods and applications has been significantly boosted by the recent developmentsin deep learning techniques. VR content creationand exploration relates to image and video analysis, synthesis and editing, so deep learning methods such as fully convolutional networks and general adversarial networks are widely employed, designed specifically to handle panoramic images and video and virtual 3D scenes. This article surveys recent research that uses such deep learning methods for VR content creation and exploration. It considers the problems involved, and discusses possible future directions in this active and emerging research area.

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Computational Visual Media
Pages 3-28

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Cite this article:
Wang M, Lyu X-Q, Li Y-J, et al. VR content creation and exploration with deep learning: A survey. Computational Visual Media, 2020, 6(1): 3-28. https://doi.org/10.1007/s41095-020-0162-z

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Revised: 30 January 2020
Accepted: 02 March 2020
Published: 23 March 2020
© The author(s) 2020

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www. editorialmanager.com/cvmj.