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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.03507 |
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| _version_ | 1866912893131292672 |
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| author | Zhang, Qiang Xiao, Tong Habeeb, Haroun Laich, Larissa Bouaziz, Sofien Snape, Patrick Zhang, Wenjing Cioffi, Matthew Zhang, Peizhao Pidlypenskyi, Pavel Lin, Winnie Ma, Luming Wang, Mengjiao Li, Kunpeng Long, Chengjiang Song, Steven Prazak, Martin Sjoholm, Alexander Deogade, Ajinkya Lee, Jaebong Mangas, Julio Delgado Aubel, Amaury |
| author_facet | Zhang, Qiang Xiao, Tong Habeeb, Haroun Laich, Larissa Bouaziz, Sofien Snape, Patrick Zhang, Wenjing Cioffi, Matthew Zhang, Peizhao Pidlypenskyi, Pavel Lin, Winnie Ma, Luming Wang, Mengjiao Li, Kunpeng Long, Chengjiang Song, Steven Prazak, Martin Sjoholm, Alexander Deogade, Ajinkya Lee, Jaebong Mangas, Julio Delgado Aubel, Amaury |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03507 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | REFA: Real-time Egocentric Facial Animations for Virtual Reality Zhang, Qiang Xiao, Tong Habeeb, Haroun Laich, Larissa Bouaziz, Sofien Snape, Patrick Zhang, Wenjing Cioffi, Matthew Zhang, Peizhao Pidlypenskyi, Pavel Lin, Winnie Ma, Luming Wang, Mengjiao Li, Kunpeng Long, Chengjiang Song, Steven Prazak, Martin Sjoholm, Alexander Deogade, Ajinkya Lee, Jaebong Mangas, Julio Delgado Aubel, Amaury Computer Vision and Pattern Recognition 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. |
| title | REFA: Real-time Egocentric Facial Animations for Virtual Reality |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.03507 |