Face Reflectance and Geometry Modeling via Differentiable Ray Tracing
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Abstract (arXiv)
We present a novel strategy to automatically reconstruct 3D faces from monocular images with explicitly disentangled facial geometry (pose, identity and expression), reflectance (diffuse and specular albedo), and self-shadows. The scene lights are modeled as a virtual light stage with pre-oriented area lights used in conjunction with differentiable Monte-Carlo ray tracing to optimize the scene and face parameters. With correctly disentangled self-shadows and specular reflection parameters, we can not only obtain robust facial geometry reconstruction, but also gain explicit control over these parameters, with several practical applications. We can change facial expressions with accurate resultant self-shadows or relight the scene and obtain accurate specular reflection and several other parameter combinations.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Oct 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracingcited by 48
- 2021Practical Face Reconstruction via Differentiable Ray Tracingcited by 35
- 2023MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shadingcited by 18
- 2022S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Imagecited by 17
- 2023MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading20 Dec 2023
- 2022S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Image15 Mar 2022
- 2021Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing29 Mar 2021
- 2021Practical Face Reconstruction via Differentiable Ray Tracing13 Jan 2021