# Relighting Humans in the Wild: Monocular Full-Body Human Relighting with Domain Adaptation

arXiv: https://arxiv.org/abs/2110.07272 · HTML: https://mlsanity.com/paper/2110.07272/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2021.

## Built on by (most cited first, 21 of 21)

- [Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation](https://mlsanity.com/paper/2209.10510/) (2022)
- [Relighting4D: Neural Relightable Human from Videos](https://mlsanity.com/paper/2207.07104/) (2022)
- [VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting](https://mlsanity.com/paper/2201.04873/) (2022)
- [Geometry-aware Single-image Full-body Human Relighting](https://mlsanity.com/paper/2207.04750/) (2022)
- [MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos](https://mlsanity.com/paper/2407.08414/) (2024)
- [De-rendering 3D Objects in the Wild](https://mlsanity.com/paper/2201.02279/) (2022)
- [ScribbleLight: Single Image Indoor Relighting with Scribbles](https://mlsanity.com/paper/2411.17696/) (2024)
- [Structured 3D Features for Reconstructing Controllable Avatars](https://mlsanity.com/paper/2212.06820/) (2022)
- [RANA: Relightable Articulated Neural Avatars](https://mlsanity.com/paper/2212.03237/) (2022)
- [Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization](https://mlsanity.com/paper/2504.03011/) (2025)
- [Relightable Neural Human Assets from Multi-view Gradient Illuminations](https://mlsanity.com/paper/2212.07648/) (2022)
- [Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting](https://mlsanity.com/paper/2304.11900/) (2023)
- [Generative Portrait Shadow Removal](https://mlsanity.com/paper/2410.05525/) (2024)
- [Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition](https://mlsanity.com/paper/2302.13279/) (2023)
- [Personalized Video Relighting With an At-Home Light Stage](https://mlsanity.com/paper/2311.08843/) (2023)
- [BEAM: Bridging Physically-based Rendering and Gaussian Modeling for Relightable Volumetric Video](https://mlsanity.com/paper/2502.08297/) (2025)
- [PI-Light: Physics-Inspired Diffusion for Full-Image Relighting](https://mlsanity.com/paper/2601.22135/) (2026)
- [Generalizable and Relightable Gaussian Splatting for Human Novel View Synthesis](https://mlsanity.com/paper/2505.21502/) (2025)
- [All-frequency Full-body Human Image Relighting](https://mlsanity.com/paper/2411.00356/) (2024)
- [HAFMat: Hybrid Priors Guided Adaptive Fusion for Single-Image Human Material Estimation](https://mlsanity.com/paper/2606.16323/) (2026)
- [RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting](https://mlsanity.com/paper/2609.07414/) (2026)

## Abstract (arXiv)

The modern supervised approaches for human image relighting rely on training data generated from 3D human models. However, such datasets are often small (e.g., Light Stage data with a small number of individuals) or limited to diffuse materials (e.g., commercial 3D scanned human models). Thus, the human relighting techniques suffer from the poor generalization capability and synthetic-to-real domain gap. In this paper, we propose a two-stage method for single-image human relighting with domain adaptation. In the first stage, we train a neural network for diffuse-only relighting. In the second stage, we train another network for enhancing non-diffuse reflection by learning residuals between real photos and images reconstructed by the diffuse-only network. Thanks to the second stage, we can achieve higher generalization capability against various cloth textures, while reducing the domain gap. Furthermore, to handle input videos, we integrate illumination-aware deep video prior to greatly reduce flickering artifacts even with challenging settings under dynamic illuminations.