Albedo Editing
By directly modifying the absorption coefficients for specific hair strands, we can achieve various color effects.
We present GroomLight, a novel method for relightable hair appearance modeling from multi-view images. Existing hair capture methods struggle to balance photorealistic rendering with relighting capabilities. Analytical material models, while physically grounded, often fail to fully capture appearance details. Conversely, neural rendering approaches excel at view synthesis but generalize poorly to novel lighting conditions. GroomLight addresses this challenge by combining the strengths of both paradigms. It employs an extended hair BSDF model to capture primary light transport and a light-aware residual model to reconstruct the remaining details. We further propose a hybrid inverse rendering pipeline to optimize both components, enabling high-fidelity relighting, view synthesis, and material editing. Extensive evaluations on real-world hair data demonstrate state-of-the-art performance of our method.
By directly modifying the absorption coefficients for specific hair strands, we can achieve various color effects.
Our representation supports rendering diverse highlight patterns by changing the roughness parameters.
@inproceedings{Zheng2025GroomLight,
title = {GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling},
author = {Zheng, Yang and Chai, Menglei and Vicini, Delio and Zhou, Yuxiao and Xu, Yinghao and Guibas, Leonidas and Wetzstein, Gordon and Beeler, Thabo},
journal={arxiv},
year={2025}
}