REFA: Real-time Egocentric Facial Animations for Virtual Reality

Qiang Zhang, Tong Xiao, Haroun Habeeb, Larissa Laich, Sofien Bouaziz, Patrick Snape, Wenjing Zhang, Matthew Cioffi, Peizhao Zhang, Pavel Pidlypenskyi, Winnie Lin, Luming Ma, Mengjiao Wang, Kunpeng Li, Chengjiang Long, Steven Song, Martin Prazak, Alexander Sjoholm, Ajinkya Deogade, Jaebong Lee, Julio Delgado Mangas, Amaury Aubel; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4793-4802

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 our machine learning model on heterogeneous data and labels coming form multiple sources e.g. 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.