Predicting the visual saliency of the people with VIMS As is known to us, visually induced motion sickness (VIMS) is often experienced in a virtual environment. Learning the visual attention of people with VIMS contributes to related research in the field of virtual reality (VR) content design and psychology. In this paper, we first construct a saliency prediction for people with VIMS (SPPV) database, which is the first of its kind. The database consists of 80 omnidirectional images and the corresponding eye tracking data collected from 30 individuals. We analyze the performance of five stateoftheart deep neural networks (DNN)based saliency prediction algorithms with their original networks and the finetuned networks on our database. We predict the atypical visual attention of people with VIMS for the first time and obtain relatively good saliency prediction results for VIMS controls so far.

Prediction of Discomfort due to Egomotion in Immersive Videos for Virtual Reality We consider the problem of automatic assessment of visually induced motion sickness in virtual reality applications. In particular, we study the impact on visual discomfort due to camera motion or egomotion present in the video displayed through a head mounted display. We develop a database of 100 short duration videos with different camera trajectories, speeds and shake levels and conduct a large scale subjective study by collecting more than 4000 human ratings of discomfort levels. The videos are generated synthetically by applying different camera trajectories. We then use the subjective study to learn to predict discomfort by designing features describing the camera motion. The features are based on the ground truth camera trajectory and estimate the camera velocity and shake and depth of the visual scene. We show that these features can be effectively used to predict discomfort by obtaining a high correlation with the subjective discomfort scores provided by humans.