VR content creation and exploration with deep learning: A survey

被引:50
作者
Wang, Miao [1 ,2 ]
Lyu, Xu-Quan [1 ]
Li, Yi-Jun [1 ]
Zhang, Fang-Lue [3 ]
机构
[1] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
[2] Peng Cheng Lab, Shenzhen 518000, Peoples R China
[3] Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington, New Zealand
基金
中国国家自然科学基金;
关键词
virtual reality; deep learning; neural networks; 360 degrees image and video virtual content; 3D FACE RECONSTRUCTION; 360-DEGREES; GENERATION; PREDICTION;
D O I
10.1007/s41095-020-0162-z
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Virtual reality (VR) offers an artificial, computer 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 developments in deep learning techniques. VR content creation and 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.
引用
收藏
页码:3 / 28
页数:26
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