Gaze Mapping for Immersive Virtual Environments Based on Image Retrieval

Abstract

In this paper, we introduce a novel gaze mapping approach for free viewing conditions in dynamic immersive virtual environments (VEs), which projects recorded eye fixation data of users, who viewed the VE from different perspectives, to the current view. This generates eye fixation maps, which can serve as ground truth for training machine learning (ML) models to predict saliency and the user’s gaze in immersive virtual reality (VR) environments. We use a flexible image retrieval approach based on SIFT features, which can also map the gaze under strong viewpoint changes and dynamic changes. A vocabulary tree enables to scale to the large amounts of data with typically several hundred thousand frames and a homography transform re-projects the fixations to the current view. To evaluate our approach, we measure the predictive quality of our eye fixation maps to model the gaze of the current user and compare our maps to computer-generated saliency maps on the DGaze and the Saliency in VR datasets. The results show that our method often outperform these saliency predictors. However, in contrast to these methods, our approach collects real fixations from human observers, and can thus serve to estimate ground truth fixation maps in dynamic VR environments, which can be used to train and evaluate gaze predictors.