mlsanityData to 7 Oct 2026

High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptation

arXiv 2103.15876 ↗First posted 29 Mar 2021cs.CV eess.IVcited by 13

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Abstract (arXiv)

3D video avatars can empower virtual communications by providing compression, privacy, entertainment, and a sense of presence in AR/VR. Best 3D photo-realistic AR/VR avatars driven by video, that can minimize uncanny effects, rely on person-specific models. However, existing person-specific photo-realistic 3D models are not robust to lighting, hence their results typically miss subtle facial behaviors and cause artifacts in the avatar. This is a major drawback for the scalability of these models in communication systems (e.g., Messenger, Skype, FaceTime) and AR/VR. This paper addresses previous limitations by learning a deep learning lighting model, that in combination with a high-quality 3D face tracking algorithm, provides a method for subtle and robust facial motion transfer from a regular video to a 3D photo-realistic avatar. Extensive experimental validation and comparisons to other state-of-the-art methods demonstrate the effectiveness of the proposed framework in real-world scenarios with variability in pose, expression, and illumination. Please visit https://www.youtube.com/watch?v=dtz1LgZR8cc for more results. Our project page can be found at https://www.cs.rochester.edu/u/lchen63.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 32 of its 59 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

32 of 59 references.

References · 32

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20 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 5; 2025: 2; 2026: 1.

13 papers.

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