# Relightify: Relightable 3D Faces from a Single Image via Diffusion Models

**Authors:** Foivos Paraperas Papantoniou, Alexandros Lattas, Stylianos Moschoglou, Stefanos Zafeiriou  
**arXiv:** [2305.06077](https://arxiv.org/abs/2305.06077)  
**Date:** 2023-05-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.06077v1/

## Key points

- Diffusion models have been successful in image generation and addressing inverse problems
- Authors present first approach to using diffusion models for 3D facial BRDF reconstruction from a single image
- High-quality UV dataset of facial reflectance used, rendered under varying illumination settings to simulate natural RGB textures
- Unconditional diffusion model trained on concatenated pairs of rendered textures and reflectance components
- Sampling from diffusion model while retaining observed texture part intact inpaints self-occluded areas and unknown reflectance components in single sequence of denoising steps
- Approach directly acquires observed texture from input image, resulting in more faithful and consistent reflectance estimation
- Superior performance demonstrated through qualitative and quantitative comparisons compared to existing methods such as TBGAN and StyleGAN-based models
- Potential applications in computer graphics, virtual reality, and augmented reality

## Summary

The use of diffusion models in image generation has been remarkably successful, and recent works have demonstrated their impressive ability to address a number of inverse problems in an unsupervised way by properly constraining the sampling process based on a conditioning input. Building on this, the authors present the first approach to using diffusion models as a prior for highly accurate 3D facial Bidirectional Reflectance Distribution Function (BRDF) reconstruction from a single image. To achieve this, they leverage a high-quality UV dataset of facial reflectance (diffuse and specular albedo and normals), which they render under varying illumination settings to simulate natural RGB textures. They then train an unconditional diffusion model on concatenated pairs of rendered textures and reflectance components. At test time, they fit a 3D morphable model to the given image and unwrap the face in a partial UV texture. By sampling from the diffusion model while retaining the observed texture part intact, the model inpaints not only the self-occluded areas but also the unknown reflectance components in a single sequence of denoising steps. In contrast to existing methods, this approach directly acquires the observed texture from the input image, resulting in more faithful and consistent reflectance estimation. Through qualitative and quantitative comparisons, superior performance is demonstrated in both texture completion as well as reflectance reconstruction tasks. Previous works such as TBGAN [23] introduced deep generative networks for facial reflectance based on ProgressiveGAN [34], while [44] introduced a more powerful model based on StyleGAN [35]. However, both works did not showcase fitting capabilities. An extension of the latter [48] introduced multiple networks with a StyleGAN2 [36] base that can be used to generate shape and albedo from images with arbitrary illumination and expression. While close to this work, it uses only one powerful diffusion model that infers not only diffuse albedo but also specular albedo and normals. Furthermore, this approach inpaints only the occluded facial areas, preserving visible parts of textures intactly. Overall, this work presents a significant advancement in 3D facial BRDF reconstruction from a single image using diffusion models as a prior. The results demonstrate superior performance compared to existing methods and have potential applications in fields such as computer graphics, virtual reality, and augmented reality.