VR Facial Expression Tracking via Action Unit Intensity Regression Model

Abstract

Virtual Reality (VR) Head-Mounted Displays (HMDs), also known as VR headsets, are powerful devices that provide interaction between people and the virtual 3D world generated by a computer. For an immersive VR experience, the realistic facial animation of the participant is crucial. However, facial expression tracking has been one of the major challenges of facial animation. Existing face tracking methods often rely on a statistical model of the entire face, which is not feasible as occlusions arising from HMDs are inevitable. In this paper, we provide an overview of the current state of VR facial expression tracking and discuss bottlenecks for VR expression re-targeting. We introduce a baseline method for expression tracking from single view, partially occluded facial infrared (IR) images, which are captured by the HP reverb G2 VR headset camera. The experiment shows good visual prediction results for mouth region expressions from a single person.