# BecomingLit: Relightable Gaussian Avatars with Hybrid Neural Shading

arXiv: https://arxiv.org/abs/2506.06271 · HTML: https://mlsanity.com/paper/2506.06271/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 58 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [gsplat: An Open-Source Library for Gaussian Splatting](https://mlsanity.com/paper/2409.06765/) (2024)
- [Mixture of Volumetric Primitives for Efficient Neural Rendering](https://mlsanity.com/paper/2103.01954/) (2021)
- [GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians](https://mlsanity.com/paper/2312.02069/) (2023)
- [General Facial Representation Learning in a Visual-Linguistic Manner](https://mlsanity.com/paper/2112.03109/) (2021)
- [NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads](https://mlsanity.com/paper/2305.03027/) (2023)
- [VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution](https://mlsanity.com/paper/2205.03409/) (2022)
- [Instant Volumetric Head Avatars](https://mlsanity.com/paper/2211.12499/) (2022)
- [Single Image Portrait Relighting](https://mlsanity.com/paper/1905.00824/) (2019)
- [Pixel Codec Avatars](https://mlsanity.com/paper/2104.04638/) (2021)
- [Relightable Gaussian Codec Avatars](https://mlsanity.com/paper/2312.03704/) (2023)
- [Bilateral Reference for High-Resolution Dichotomous Image Segmentation](https://mlsanity.com/paper/2401.03407/) (2024)
- [Neural Light Transport for Relighting and View Synthesis](https://mlsanity.com/paper/2008.03806/) (2020)
- [Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation](https://mlsanity.com/paper/2209.10510/) (2022)
- [3D Gaussian Blendshapes for Head Avatar Animation](https://mlsanity.com/paper/2404.19398/) (2024)
- [NPGA: Neural Parametric Gaussian Avatars](https://mlsanity.com/paper/2405.19331/) (2024)
- [HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field](https://mlsanity.com/paper/2309.17128/) (2023)
- [VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations](https://mlsanity.com/paper/2208.00949/) (2022)
- [DifFRelight: Diffusion-Based Facial Performance Relighting](https://mlsanity.com/paper/2410.08188/) (2024)
- [Towards Practical Capture of High-Fidelity Relightable Avatars](https://mlsanity.com/paper/2309.04247/) (2023)
- [Relightable Full-Body Gaussian Codec Avatars](https://mlsanity.com/paper/2501.14726/) (2025)
- [High-Res Facial Appearance Capture from Polarized Smartphone Images](https://mlsanity.com/paper/2212.01160/) (2022)

## Built on by (most cited first, 6 of 6)

- [LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes](https://mlsanity.com/paper/2601.15283/) (2026)
- [Light4D: Training-Free Extreme Viewpoint 4D Video Relighting](https://mlsanity.com/paper/2602.11769/) (2026)
- [AvatarBack: Back-Head Generation for Complete 3D Avatars from Front-View Images](https://mlsanity.com/paper/2508.20623/) (2025)
- [POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling](https://mlsanity.com/paper/2512.13192/) (2025)
- [RelightAnyone: A Generalized Relightable 3D Gaussian Head Model](https://mlsanity.com/paper/2601.03357/) (2026)
- [Pixel Cube: Diffusion-based Portrait Video Relighting Through Realistic Lighting Reproduction](https://mlsanity.com/paper/2606.02919/) (2026)

## Abstract (arXiv)

We introduce BecomingLit, a novel method for reconstructing relightable, high-resolution head avatars that can be rendered from novel viewpoints at interactive rates. Therefore, we propose a new low-cost light stage capture setup, tailored specifically towards capturing faces. Using this setup, we collect a novel dataset consisting of diverse multi-view sequences of numerous subjects under varying illumination conditions and facial expressions. By leveraging our new dataset, we introduce a new relightable avatar representation based on 3D Gaussian primitives that we animate with a parametric head model and an expression-dependent dynamics module. We propose a new hybrid neural shading approach, combining a neural diffuse BRDF with an analytical specular term. Our method reconstructs disentangled materials from our dynamic light stage recordings and enables all-frequency relighting of our avatars with both point lights and environment maps. In addition, our avatars can easily be animated and controlled from monocular videos. We validate our approach in extensive experiments on our dataset, where we consistently outperform existing state-of-the-art methods in relighting and reenactment by a significant margin.