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

• URL: http://arxiv.org/abs/2103.15876v1
• Date: Mon, 29 Mar 2021 18:33:49 GMT
• Title: High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptation
• Authors: Lele Chen, Chen Cao, Fernando De la Torre, Jason Saragih, Chenliang Xu, Yaser Sheikh
• Abstract summary: 3D video avatars can empower virtual communications by providing compression, privacy, entertainment, and a sense of presence in AR/VR. Existing person-specific 3D models are not robust to lighting, hence their results typically miss subtle facial behaviors and cause artifacts in the avatar. 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.
• Score: 117.32310997522394
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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