DeepSpace: Mood-Based Image Texture Generation for Virtual Reality from Music

Abstract

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.

Affective computing in the context of music therapy: a systematic review

“…Then, they perform emotion recognition methods through electroencephalographic signals (T. Teo & Chia, 2018). In another case, the authors use VR scenes along with music as stimuli to induce emotional responses in individuals (Sra et al, 2017). It emphasizes that, although VR were not designed for this purpose, it stands out positively when compared to traditional approaches.…”

“…Finally, it is important to point out that only 9 studies, among the 33 studies mentioned in this subsection, used Artificial Intelligence techniques (T. Aranha et al, 2017;Song et al, 2019;Murad et al, 2017;Cavallo et al, 2020;Teo & Chia, 2018;Lyu & Yuan, 2020;Sra et al, 2017). The mostly used techniques were Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN), Decision Trees and K-Nearest Neighbor (KNN).…”