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VR content creation and exploration with deep learning: A survey

  • Miao Wang*
  • , Xu Quan Lyu
  • , Yi Jun Li
  • , Fang Lue Zhang
  • *Corresponding author for this work
  • Peng Cheng Laboratory
  • Beihang University
  • Victoria University of Wellington

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)3-28
Number of pages26
JournalComputational Visual Media
Volume6
Issue number1
DOIs
StatePublished - 1 Mar 2020

Keywords

  • 360° image and video virtual content
  • deep learning
  • neural networks
  • virtual reality

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