Real-Walk Modelling: Deep Learning Model for User Mobility in Virtual Reality

Abstract

Understanding human interactions in virtual reality (VR) can help developing intelligent applications that adapt to users' needs and enhance the user experience. The significant development of VR content has expanded the impact on VR complexity, making understanding VR spatial characteristics more difficult. While user mobility is a crucial part of their interactions with the VR environment, the current literature still does not provide a suitable framework to interpret and model VR user mobility data. We conducted a user experiment in the context of an abstract VR painting exhibition where users are prompted to walk naturally in a physical area to explore the VR painting. Deep Learning models are used to model user mobility sequences and predict their future movements while engaging with the art exhibition. Our user mobility model can support the development of new VR applications for the improved user navigation and social experience in VR.