GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling

2025-03-13

AI Summary

Existing hair modeling approaches struggle to simultaneously achieve physical plausibility and illumination generalization: analytical BSDF models lack sufficient detail to capture complex scattering phenomena, while neural rendering methods exhibit limited relighting capability. To address this, GroomLight introduces the first hybrid inverse rendering framework that jointly optimizes an extended hair BSDF—explicitly modeling anisotropic scattering and multi-layer microstructure—with an illumination-aware neural residual network. This coupling enables synergistic learning of physics-based priors and data-driven residuals within a unified optimization pipeline. The method supports high-fidelity relighting, novel-view synthesis, and editable material manipulation. Evaluated on real human hair strand data, GroomLight achieves state-of-the-art performance, significantly improving illumination transfer fidelity and recovering fine-scale structural details with unprecedented accuracy.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Balances photorealistic rendering with relighting capabilities
• Combines analytical and neural rendering for better generalization
• Enables high-fidelity relighting, view synthesis, and material editing

Innovation

• Combines analytical and neural rendering techniques
• Uses extended hair BSDF for light transport
• Implements hybrid inverse rendering pipeline