REFA: Real-time Egocentric Facial Animations for Virtual Reality
This enables non-intrusive facial animation for VR users in applications like gaming and video conferencing, representing a domain-specific incremental improvement.
The paper tackles real-time facial expression tracking in VR using egocentric infrared cameras, achieving accurate virtual character animation without intrusive calibration by training a model on 18k diverse subjects with a novel distillation approach and differentiable rendering pipeline.
We present a novel system for real-time tracking of facial expressions using egocentric views captured from a set of infrared cameras embedded in a virtual reality (VR) headset. Our technology facilitates any user to accurately drive the facial expressions of virtual characters in a non-intrusive manner and without the need of a lengthy calibration step. At the core of our system is a distillation based approach to train a machine learning model on heterogeneous data and labels coming form multiple sources, \eg synthetic and real images. As part of our dataset, we collected 18k diverse subjects using a lightweight capture setup consisting of a mobile phone and a custom VR headset with extra cameras. To process this data, we developed a robust differentiable rendering pipeline enabling us to automatically extract facial expression labels. Our system opens up new avenues for communication and expression in virtual environments, with applications in video conferencing, gaming, entertainment, and remote collaboration.