ESTIMATING VR SICKNESS CAUSED BY CAMERA SHAKE IN VR VIDEOGRAPHY

Recent development of Virtual Reality (VR) technology provides more realistic experience for viewers with a variety of contents. While the viewing safety of the viewers is one of the important issues in VR industry, the necessity of VR sickness estimation has been drawing attentions. Inspired by the observations that camera shake in VR videography is one of the major causes of VR sickness, we propose a novel deep network that predicts VR sickness level of individuals caused by camera shake. The proposed method is designed to comprehensively identify changes in direction and speed of the VR video scenes with camera shake. Sparse selection of optical flow maps with different intervals allows the proposed network to efficiently extract stimulus features with a variety of camera shake patterns. We built a new benchmark database for the evaluation of the proposed method that consists of 360-degree videos including various camera shake movements, physiological signals, and Simulation Sickness Questionnaires (SSQ) scores of the experimental participants. Experimental results of the sickness prediction show the effectiveness of the proposed method on the built benchmark database.