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Computational Visual Media 2020, 6(1): 3-28 https://doi.org/10.1007/s41095-020-0162-z
Review Article | Open Access | Issue | Published: 23 March 2020

VR content creation and exploration with deep learning: A survey

Show Author's Information Miao Wang1,2( ),  Xu-Quan Lyu1,  Yi-Jun Li1,  Fang-Lue Zhang3
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.
Keywords:
deep learning, virtual reality, neural net-works, 360∘ image and video virtual content
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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Abstract Full text About this article

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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About this article

VR content creation and exploration with deep learning: A survey

Show Author's information Miao Wang1,2( ), Xu-Quan Lyu1, Yi-Jun Li1, Fang-Lue Zhang3
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.

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.

Keywords: deep learning, virtual reality, neural net-works, 360∘ image and video virtual content

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Revised: 30 January 2020
Accepted: 02 March 2020
Published: 23 March 2020
Issue date: March 2020

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© The author(s) 2020

Acknowledgements

The authors would like to thank the reviewers. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61902012, 61932003). Fang-Lue Zhang was supported by a Victoria Early-Career Research Excellence Award.

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