A Collection of Papers and Codes about Hair and Fur Modeling
SIGGRAPH2024 [back]
Real-time Physically Guided Hair Interpolation
- Paper: https://dl.acm.org/doi/pdf/10.1145/3658176
- Tags: Hair Interpolation, Cosserat Rod
- Abstract:
Linear interpolation can often cause severe deformation artifacts in hair for which alternatives often entail expensive training or precomputation. Instead, we present a novel force-based hair interpolation scheme to leverage existing real-time simulation data for a robust interpolation with very little overhead and no expensive training.
Real-Time Hair Rendering with Hair Meshes
- Paper: https://dl.acm.org/doi/pdf/10.1145/3641519.3657521
- Project Website: http://cemyuksel.com/research/hairmesh_rendering/
- Tags: Real-time rendering, hair rendering, hair modeling, hair meshes
- Abstract:
Hair meshes are known to be effective for modeling and animating hair in computer graphics. We present how the hair mesh structure can be used for efficiently rendering strand-based hair models on the GPU with on-the-fly geometry generation that provides orders of magnitude reduction in storage and memory bandwidth. We use mesh shaders to carefully distribute the computation and a custom texture layout for offloading a part of the computation to the hardware texture units. We also present a set of procedural styling operations to achieve hair strand variations for a wide range of hairstyles and a consistent coordinate-frame generation approach to attach these variations to an animating/deforming hair mesh. Finally, we describe level-of-detail techniques for improving the performance of rendering distant hair models. Our results show an unprecedented level of performance with strand-based hair rendering, achieving hundreds of full hair models animated and rendered at real-time frame rates on a consumer GPU.
Modeling Hair Strands with Roving Capsules
- Paper: https://dl.acm.org/doi/pdf/10.1145/3641519.3657450
- Project Website: https://research.nvidia.com/publication/2024-07_modeling-hair-strands-roving-capsules
- Code: https://www.shadertoy.com/view/4ffXWs
- Tags: Bézier curves, parametric surfaces, swept volumes, generalized cylinders, hair and fur rendering, ray tracing, polynomial equations
- Abstract:
Hair strands can be modeled by sweeping spheres with varying radii along Bézier curves. We ray-trace such shapes by finding intersections of a given ray with a set of capsules dynamically defined at runtime. A substantial performance boost is achieved by systematically eliminating parts of the shape that are guaranteed not to intersect with the given ray. The new intersector is more than twice faster than the previously leading phantom algorithm. This improvement results in a 30% overall performance increase, which includes traversal, shading, and the rendering system overhead. In addition, we derive a parametric form of the swept sphere shapes. This provides a deeper understanding of the properties of such objects compared to the offset surfaces obtained by sweeping circles orthogonal to a given curve.
SIGGRAPH2023 [back]
Sag-free Initialization for Strand-based Hybrid Hair Simulation
- Paper: https://graphics.cs.utah.edu/research/projects/sag-free-hair/sig23_sagfree-hair.pdf
- Project Website: https://graphics.cs.utah.edu/research/projects/sag-free-hair/
- Tags: Hair Simulation, Strand-based
- Abstract:
This paper proposes a novel four-stage, sag-free initialization framework to solve stable quasistatic configurations for hybrid, strand-based hair dynamic systems. Our results show that our method successfully prevents sagging on various hairstyles and has minimal impact on the hair motion during simulation.
CT2Hair: High-Fidelity 3D Hair Modeling using Computed Tomography
- Code: https://github.com/facebookresearch/CT2Hair
- Project Website: http://yuefanshen.net/CTHair
- Abstract:
We introduce CT2Hair, a fully automatic framework for creating high-fidelity 3D hair models using computed tomography. Our approach utilizes real-world hair wigs as input and is able to reconstruct hair strands for a wide range of hair styles. The 3D hair models are suitable for use in downstream graphics applications.
Interactive Hair Simulation on the GPU Using ADMM
- Paper: https://d1qx31qr3h6wln.cloudfront.net/publications/Interactive%20Hair%20Simulation%20on%20the%20GPU%20Using%20ADMM.pdf
- Project Website: https://research.nvidia.com/publication/2023-08_interactive-hair-simulation-gpu-using-admm
- Abstract:
We design and validate a local-global solver dedicated to the simulation of Kirchhoff rods with Coulomb friction that can fully leverage the massively parallel compute capabilities of moderns GPUs, enabling interactive physics-based simulation and authoring of virtual grooms.
A Practical Wave Optics Reflection Model for Hair and Fur
- Paper: https://mandyxmq.github.io/research/assets/WaveFiber3d_paper_compress.pdf
- Project Website: https://mandyxmq.github.io/research/wavefiber_3d.html
- Abstract:
We propose a practical model for simulating wave effects from rough fibers with arbitrary 3D microgeometry. This model can be easily combined with existing scattering models to render hair and fur of various colors, adding colorful glints that were missing from previous models.
NeRF-Texture: Texture Synthesis with Neural Radiance Fields
- Paper: [Coming Soon]
- Code: https://github.com/yihua7/NeRF-Texture
- Project Website: https://yihua7.github.io/NeRF-Texture-web/
- Abstract:
We propose NeRF-Texture, a novel texture synthesis method based on NeRF. It effectively models real-world textures containing both meso-scale geometry and view-dependent appearance by utilizing a coarse-fine disentanglement representation and synthesizes NeRF textures of arbitrary sizes via patch matching, which can be applied to new surfaces to add rich details.
ECCV2022 [back]
Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images
- Paper: https://arxiv.org/pdf/2207.14067.pdf
- Project Website: https://radualexandru.github.io/neural_strands/
- Abstract:
We present Neural Strands, a novel learning framework for modeling accurate hair geometry and appearance from multi-view image inputs. The learned hair model can be rendered in real-time from any viewpoint with high-fidelity view-dependent effects. Our model achieves intuitive shape and style control unlike volumetric counterparts. To enable these properties, we propose a novel hair representation based on a neural scalp texture that encodes the geometry and appearance of individual strands at each texel location. Furthermore, we introduce a novel neural rendering framework based on rasterization of the learned hair strands. Our neural rendering is strand-accurate and anti-aliased, making the rendering view-consistent and photorealistic. Combining appearance with a multi-view geometric prior, we enable, for the first time, the joint learning of appearance and explicit hair geometry from a multi-view setup. We demonstrate the efficacy of our approach in terms of fidelity and efficiency for various hairstyles.