# Monocular Reconstruction of Neural Face Reflectance Fields

arXiv: https://arxiv.org/abs/2008.10247 · HTML: https://mlsanity.com/paper/2008.10247/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 20 of 20)

- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)
- [Neural Video Portrait Relighting in Real-time via Consistency Modeling](https://mlsanity.com/paper/2104.00484/) (2021)
- [Face Relighting with Geometrically Consistent Shadows](https://mlsanity.com/paper/2203.16681/) (2022)
- [DifFRelight: Diffusion-Based Facial Performance Relighting](https://mlsanity.com/paper/2410.08188/) (2024)
- [Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image](https://mlsanity.com/paper/2403.09632/) (2024)
- [Lite2Relight: 3D-aware Single Image Portrait Relighting](https://mlsanity.com/paper/2407.10487/) (2024)
- [A Light Stage on Every Desk](https://mlsanity.com/paper/2105.08051/) (2021)
- [Learning a 3D Morphable Face Reflectance Model from Low-cost Data](https://mlsanity.com/paper/2303.11686/) (2023)
- [EyeNeRF: A Hybrid Representation for Photorealistic Synthesis, Animation and Relighting of Human Eyes](https://mlsanity.com/paper/2206.08428/) (2022)
- [DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows](https://mlsanity.com/paper/2304.09479/) (2023)
- [Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization](https://mlsanity.com/paper/2504.03011/) (2025)
- [3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling](https://mlsanity.com/paper/2209.07366/) (2022)
- [3DPR: Single Image 3D Portrait Relight using Generative Priors](https://mlsanity.com/paper/2510.15846/) (2025)
- [A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization](https://mlsanity.com/paper/2310.03205/) (2023)
- [Light Sampling Field and BRDF Representation for Physically-based Neural Rendering](https://mlsanity.com/paper/2304.05472/) (2023)
- [PhotoApp: Photorealistic Appearance Editing of Head Portraits](https://mlsanity.com/paper/2103.07658/) (2021)
- [INFAMOUS-NeRF: ImproviNg FAce MOdeling Using Semantically-Aligned Hypernetworks with Neural Radiance Fields](https://mlsanity.com/paper/2312.16197/) (2023)
- [Neural Relighting and Expression Transfer On Video Portraits](https://mlsanity.com/paper/2107.14735/) (2021)
- [POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling](https://mlsanity.com/paper/2512.13192/) (2025)

## Abstract (arXiv)

The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a specular component. This still leaves out important perceptual aspects of reflectance as higher-order global illumination effects and self-shadowing are not modeled. We present a new neural representation for face reflectance where we can estimate all components of the reflectance responsible for the final appearance from a single monocular image. Instead of modeling each component of the reflectance separately using parametric models, our neural representation allows us to generate a basis set of faces in a geometric deformation-invariant space, parameterized by the input light direction, viewpoint and face geometry. We learn to reconstruct this reflectance field of a face just from a monocular image, which can be used to render the face from any viewpoint in any light condition. Our method is trained on a light-stage training dataset, which captures 300 people illuminated with 150 light conditions from 8 viewpoints. We show that our method outperforms existing monocular reflectance reconstruction methods, in terms of photorealism due to better capturing of physical premitives, such as sub-surface scattering, specularities, self-shadows and other higher-order effects.