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GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians (2402.10483v1)

Published 16 Feb 2024 in cs.GR and cs.CV

Abstract: 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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Citations (18)
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Summary

  • The paper introduces GaussianHair, modeling hair strands as connected 3D Gaussian primitives to capture geometric and appearance fidelity.
  • It presents a specialized scattering model that simulates light-hair interactions by approximating the Marschner Hair Model for enhanced realism.
  • The study also introduces the RealHair dataset, a high-resolution collection that supports advanced research in digital human hair rendering.

Advancements in Hair Modeling and Rendering with GaussianHair

Introduction to GaussianHair

In the evolving landscape of computer graphics, the quest for high-fidelity digital human representations remains a pivotal challenge, particularly in the domain of realistic hair modeling and real-time rendering. Traditional methods often fall short in replicating the intricate details and dynamic qualities of human hair, necessitating labor-intensive processes or sophisticated equipment. Leveraging the latest advancements, this paper introduces GaussianHair, an innovative approach to hair modeling and rendering that seeks to transcend these limitations by achieving exceptional geometric and appearance fidelity.

The GaussianHair Model

GaussianHair is predicated on the concept of representing hair strands as sequences of connected cylindrical 3D Gaussian primitives. This design choice efficiently captures the structural and visual nuances of hair, paving the way for accurate and differentiable volumetric rendering. GaussianHair is not merely a geometric model; it also includes a specialized scattering model tailored for hair—GaussianHair Scattering Model. This model adeptly simulates the light interaction with hair, enhancing the realism of rendered images.

Geometric Representation

At its core, GaussianHair maintains the geometry and appearance characteristics of hair strands through a series of cylindrical 3D Gaussians. These Gaussians are optimized for precise position, orientation, and length, facilitating efficient projection onto a 2D plane for rendering purposes. This explicit geometric representation enables the detailed modeling of hair from images and videos, filling a significant gap in current hair modeling research.

Scattering and Appearance

The visual fidelity of hair in digital rendering relies heavily on accurate light interaction modeling. GaussianHair’s scattering model faithfully replicates these interactions, employing an approximation of the Marschner Hair Model adapted from Unreal Engine 4. This approach ensures that the rendered hair not only looks realistic in terms of structure but also in how it interacts with light, offering a significant advancement over prior methods.

RealHair Dataset

To further the research and development in hair modeling, this paper also presents the RealHair dataset. This dataset is a comprehensive collection of diverse human hairstyles, meticulously compiled to advance the paper of realistic hair reproduction. Each entry in the dataset is accompanied by high-resolution video and detailed strand geometry, fostering a deeper understanding and appreciation of human hair diversity.

Applications and Future Directions

GaussianHair’s explicit representation and advanced scattering model render it highly versatile for various applications within the CG pipeline. This includes robust editing capabilities, relighting under diverse conditions, and dynamic rendering of hair movement. These functionalities demonstrate GaussianHair’s potential to significantly enhance the production of digital human assets.

Prospective Advances

While GaussianHair marks a significant step forward, potential avenues for refinement exist, particularly in improving the scattering model’s physical accuracy and automating the adjustment of physical properties like hair roughness. Future efforts could also explore more complex hairstyles, leveraging generative models to reconstruct internal structures for styles like braids or coils.

Conclusion

GaussianHair represents a notable leap in the modeling and rendering of human hair, striking an optimal balance between geometric accuracy and rendering quality. By introducing an explicit volumetric representation complemented by a sophisticated light interaction model, this approach sets a new benchmark for realism in digital hair rendering. Coupled with the comprehensive RealHair dataset, GaussianHair not only enhances current digital human rendering capabilities but also opens new pathways for research and application in computer graphics.

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