VRSA Net: VR Sickness Assessment Considering Exceptional Motion for 360° VR Video

TLDR

VR sickness is a major safety concern when viewing immersive VR content. The authors propose a deep generative model–based VR sickness assessment network to automatically predict VR sickness scores. The VRSA network trains a VR video generator on non‑exceptional motion videos to learn tolerance, then uses the difference between original and generated videos to capture exceptional motion, and projects this difference onto a subjective score space to estimate sickness. Experimental results show the VRSA network achieves a high correlation with human perceptual VR sickness scores.

Abstract

The viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness could occur when watching immersive VR content. To deal with the viewing safety for VR content, objective assessment of VR sickness is of great importance. In this paper, we propose a novel objective VR sickness assessment (VRSA) network based on deep generative model for automatically predicting the VR sickness score. The proposed method takes into account motion patterns of VR videos in which an exceptional motion is a critical factor inducing excessive VR sickness in human motion perception. The proposed VRSA network consists of two parts, which are VR video generator and VR sickness score predictor. By training the VR video generator with common videos with non-exceptional motion, the generator learns the tolerance of VR sickness in human motion perception. As a result, the difference between the original and the generated videos by the VR video generator could represent exceptional motion of VR video causing VR sickness. In the VR sickness score predictor, the VR sickness score is predicted by projecting the difference between the original and the generated videos onto the subjective score space. For the evaluation of VR sickness assessment, we built a new dataset which consists of 360° videos (stimuli), corresponding physiological signals, and subjective questionnaires from subjective assessment experiments. Experimental results demonstrated that the proposed VRSA network achieved a high correlation with human perceptual score for VR sickness.