GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling
Journal:
arXiv
Published Date:
Mar 13, 2025
Abstract
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.
