Enhancing VR Gaming Experience using Computational Attention Models and Eye-Tracking

Abstract

This study explores the potential of enhancing interaction experiences, such as virtual reality (VR) games, through the use of computational attention models. Our proposed approach utilizes a saliency map generated by attention models to dynamically adjust game difficulty levels and to help in the game level design, resulting in a more immersive and engaging experience for users. To inform the development of this approach, we present an experimental setup that is able tp collect data in a VR environment and intends to be able to validate the adaptation of attention models to this domain. Through this work, we aim to create a framework for VR game design that leverages attention models to offer a new level of immersion and engagement for users. We believe our contributions have significant potential to enhance VR experiences and advance the field of game design.