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GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians
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Hairstyle reflects culture and ethnicity at first glance. In the digital era, various realistic human hairstyles are also critical to high-fidelity digital human assets for beauty and inclusivity. Yet, realistic hair modeling and real-time rendering for animation is a formidable challenge due to its sheer number of strands, complicated structures of geometry, and sophisticated interaction with light. This paper presents GaussianHair, a novel explicit hair representation. It enables comprehensive modeling of hair geometry and appearance from images, fostering innovative illumination effects and dynamic animation capabilities. At the heart of GaussianHair is the novel concept of representing each hair strand as a sequence of connected cylindrical 3D Gaussian primitives. This approach not only retains the hair's geometric structure and appearance but also allows for efficient rasterization onto a 2D image plane, facilitating differentiable volumetric rendering. We further enhance this model with the "GaussianHair Scattering Model", adept at recreating the slender structure of hair strands and accurately capturing their local diffuse color in uniform lighting. Through extensive experiments, we substantiate that GaussianHair achieves breakthroughs in both geometric and appearance fidelity, transcending the limitations encountered in state-of-the-art methods for hair reconstruction. Beyond representation, GaussianHair extends to support editing, relighting, and dynamic rendering of hair, offering seamless integration with conventional CG pipeline workflows. Complementing these advancements, we have compiled an extensive dataset of real human hair, each with meticulously detailed strand geometry, to propel further research in this field.
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Cited by 8 Pith papers
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GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar
GeoAvatar improves 3D head avatar quality by adaptively regulating Gaussian offsets per facial region, adding a detailed mouth structure with part-wise deformation, and releasing a new expressive monocular dataset, Dy...
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CGHair: Compact Gaussian Hair Reconstruction with Card Clustering
Hierarchical card clustering plus shared Gaussian texture codebooks reconstructs multi-view hair with 200x lower memory and 4x faster strand generation while matching prior 3DGS visual quality.
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Im2Haircut: Single-view Strand-based Hair Reconstruction for Human Avatars
A synthetic-and-real trained transformer prior plus Gaussian-splatting optimization reconstructs strand-based 3D hairstyles from a single photograph.
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HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars
HairCUP trains a universal 3D avatar prior with separately modeled face and hair, using synthetic bald images, enabling hairstyle swapping and few-shot personalization.
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HairGS: Hair Strand Reconstruction based on 3D Gaussian Splatting
HairGS reconstructs 3D hair strands from multi-view images in about one hour by fitting 3D Gaussians, merging them into strands with distance and direction rules, and refining them against the photos.
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Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation
An interactive system that combines text-based 3D hair retrieval, real-time simulation, grooming, and AI rendering to let users rapidly prototype hairstyles.
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RaRa Clipper: A Clipper for Gaussian Splatting Based on Ray Tracer and Rasterizer
RaRa Clipper pre-classifies Gaussians by distance to the clip plane, ray-traces only those near the boundary, and attenuates their opacity by the visible ray-segment fraction, giving smooth real-time clipping.
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Hybrid Mesh-Gaussian Representation for Efficient Indoor Scene Reconstruction
A hybrid representation routes texture-rich flat indoor regions to a textured mesh and keeps Gaussians only for complex geometry, reducing Gaussian counts by 18-50% with roughly comparable rendering quality.
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