Computer Graphics
TU Braunschweig

Cybersickness Reduction via Gaze-Contingent Image Deformation

Cybersickness Reduction via Gaze-Contingent Image Deformation

Virtual reality has ushered in a revolutionary era of immersive content perception. However, a persistent challenge in dynamic environments is the occurrence of cybersickness arising from a conflict between visual and vestibular cues. Prior techniques have demonstrated that limiting illusory self-motion, so-called vection, by blurring the peripheral part of images, introducing tunnel vision, or altering the camera path can effectively reduce the problem. Unfortunately, these methods often alter the user's experience with visible changes to the content. In this paper, we propose a new technique for reducing vection and combating cybersickness by subtly lowering the screen-space speed of objects in the user's peripheral vision. The method is motivated by our hypothesis that small modifications to the objects' velocity in the periphery and geometrical distortions in the peripheral vision can remain unnoticeable yet lead to reduced vection. This paper describes the experiments supporting this hypothesis and derives its limits. Furthermore, we present a method that exploits these findings by introducing subtle, screen-space geometrical distortions to animation frames to counteract the motion contributing to vection. We implement the method as a real-time post-processing step that can be integrated into existing rendering frameworks. The final validation of the technique and comparison to an alternative approach confirms its effectiveness in reducing cybersickness.

Author(s):Colin Groth, Marcus Magnor, Steve Grogorick, Martin Eisemann, Piotr Didyk
Published:to appear
Journal:ACM Transactions on Graphics Vol. 43
Presented at:SIGGRAPH 2024

  title = {Cybersickness Reduction via Gaze-Contingent Image Deformation},
  author = {Groth, Colin and Magnor, Marcus and Grogorick, Steve and Eisemann, Martin and Didyk, Piotr},
  journal = {{ACM} Transactions on Graphics},
  doi = {10.1145/3658138},
  volume = {43},
  number = {4},
  year = {2024}