Head Movement Modeling for Immersive Visualization in VR

TL;DR

This paper introduces a data-driven method to predict the future head movement volume in VR, enabling more efficient pre-caching of visual data and improving rendering performance in immersive environments.

Contribution

It presents the first predictive model for head movement volume in VR using a binned-ellipsoid technique, significantly reducing the pre-caching volume with minimal accuracy loss.

Findings

Reduces head movement prediction volume from 1m3 to 10cm3

Maintains negligible accuracy loss in predictions

Enhances pre-caching efficiency for VR rendering

Abstract

Virtual Reality, and Extended Reality in general, connect the physical body with the virtual world. Movement of our body translates to interactions with this virtual world. Only by moving our head will we see a different perspective. By doing so, the physical restrictions of our body's movement restrict our capabilities virtually. By modelling the capabilities of human movement, render engines can get useful information to pre-cache visual texture information or immersive light information. Such pre-caching becomes vital due to ever increasing realism in virtual environments. This work is the first work to predict the volume in which the head will be positioned in the future based on a data-driven binned-ellipsoid technique. The proposed technique can reduce a 1m3 volume to a size of 10cm3 with negligible accuracy loss. This volume then provides the render engine with the necessary information to pre-cache visual data.