Analysis of Intended Viewing Area vs Estimated Saliency on Narrative Plot Structures in VR Film In cinematic virtual reality film one of the primary challenges from a storytelling perceptive is that of leading the attention of the viewers to ensure that the narrative is understood as desired. Methods from traditional cinema have been applied to varying levels of success. This paper explores the use of a saliency convolutional neural network model and measures itxe2x80x99s results against the intending viewing area as denoted by the creators and the ground truth as to where the viewers actually looked. This information could then be used to further increase the effectiveness of a directorxe2x80x99s ability to focus attention in cinematic VR.

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.