DeepSpace: Mood-Based Image Texture Generation for Virtual Reality from Music
Name
Roy_DeepSpace_Mood-Based_Image_CVPR_2017_paper.pdf
Description
Published version
Size
608.23 KB
Format
Adobe PDF
Checksum (MD5)
4159a3cbbf9aafea7dae796168a8c58c
Author(s)
Sra, Misha
Vijayaraghavan, Prashanth
Rudovic, Ognjen
Maes, Pattie
Roy, Deb
Date Issued
July 2017
Publisher
IEEE
Citation
Sra, Misha, Vijayaraghavan, Prashanth, Rudovic, Ognjen, Maes, Pattie and Roy, Deb. 2017. "DeepSpace: Mood-Based Image Texture Generation for Virtual Reality from Music."
Version
Final published version
Abstract
© 2017 IEEE. Affective virtual spaces are of interest for many VR applications in areas of wellbeing, art, education, and entertainment. Creating content for virtual environments is a laborious task involving multiple skills like 3D modeling, texturing, animation, lighting, and programming. One way to facilitate content creation is to automate sub-processes like assignment of textures and materials within virtual environments. To this end, we introduce the DeepSpace approach that automatically creates and applies image textures to objects in procedurally created 3D scenes. The main novelty of our DeepSpace approach is that it uses music to automatically create kaleidoscopic textures for virtual environments designed to elicit emotional responses in users. Specifically, DeepSpace exploits the modeling power of deep neural networks, which have shown great performance in image generation tasks, to achieve mood-based image generation. Our study results indicate the virtual environments created by DeepSpace elicit positive emotions and achieve high presence scores.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvprw.2017.283
