# Quaffure: Real-Time Quasi-Static Neural Hair Simulation

arXiv: https://arxiv.org/abs/2412.10061 · HTML: https://mlsanity.com/paper/2412.10061/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 19 of 19)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SNUG: Self-Supervised Neural Dynamic Garments](https://mlsanity.com/paper/2204.02219/) (2022)
- [HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics](https://mlsanity.com/paper/2212.07242/) (2022)
- [GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians](https://mlsanity.com/paper/2402.10483/) (2024)
- [Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction](https://mlsanity.com/paper/2306.05872/) (2023)
- [NTopo: Mesh-free Topology Optimization using Implicit Neural Representations](https://mlsanity.com/paper/2102.10782/) (2021)
- [DrapeNet: Garment Generation and Self-Supervised Draping](https://mlsanity.com/paper/2211.11277/) (2022)
- [Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images](https://mlsanity.com/paper/2207.14067/) (2022)
- [Neural Cloth Simulation](https://mlsanity.com/paper/2212.11220/) (2022)
- [Human Hair Reconstruction with Strand-Aligned 3D Gaussians](https://mlsanity.com/paper/2409.14778/) (2024)
- [NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations](https://mlsanity.com/paper/2205.04175/) (2022)
- [NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation](https://mlsanity.com/paper/2212.00613/) (2022)
- [HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles](https://mlsanity.com/paper/2312.11666/) (2023)
- [HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture](https://mlsanity.com/paper/2112.06904/) (2021)
- [GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations](https://mlsanity.com/paper/2311.02062/) (2023)
- [Perm: A Parametric Representation for Multi-Style 3D Hair Modeling](https://mlsanity.com/paper/2407.19451/) (2024)
- [GroomCap: High-Fidelity Prior-Free Hair Capture](https://mlsanity.com/paper/2409.00831/) (2024)
- [Data-Free Learning of Reduced-Order Kinematics](https://mlsanity.com/paper/2305.03846/) (2023)
- [NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory](https://mlsanity.com/paper/2308.12970/) (2023)

## Built on by (most cited first, 10 of 10)

- [Neuralocks: Real-Time Dynamic Neural Hair Simulation](https://mlsanity.com/paper/2507.05191/) (2025)
- [DGH: Dynamic Gaussian Hair](https://mlsanity.com/paper/2512.17094/) (2025)
- [PhysHead: Simulation-Ready Gaussian Head Avatars](https://mlsanity.com/paper/2604.06467/) (2026)
- [HairFormer: Transformer-Based Dynamic Neural Hair Simulation](https://mlsanity.com/paper/2507.12600/) (2025)
- [Neural Modular Physics for Elastic Simulation](https://mlsanity.com/paper/2512.15083/) (2025)
- [HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion](https://mlsanity.com/paper/2602.11117/) (2026)
- [HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning](https://mlsanity.com/paper/2605.20460/) (2026)
- [PoseShield: Neural Collision Fields for Human Self-Collision Resolution](https://mlsanity.com/paper/2606.29686/) (2026)
- [Detangled: A Framework for Creating, Editing, and Inferencing Feature Rich Hair Strands](https://mlsanity.com/paper/2607.09811/) (2026)
- [Head Avatars with Dynamic Explicit Hair](https://mlsanity.com/paper/2607.23861/) (2026)

## Abstract (arXiv)

Realistic hair motion is crucial for high-quality avatars, but it is often limited by the computational resources available for real-time applications. To address this challenge, we propose a novel neural approach to predict physically plausible hair deformations that generalizes to various body poses, shapes, and hairstyles. Our model is trained using a self-supervised loss, eliminating the need for expensive data generation and storage. We demonstrate our method's effectiveness through numerous results across a wide range of pose and shape variations, showcasing its robust generalization capabilities and temporally smooth results. Our approach is highly suitable for real-time applications with an inference time of only a few milliseconds on consumer hardware and its ability to scale to predicting the drape of 1000 grooms in 0.3 seconds. Please see our project page here following https://tuurstuyck.github.io/quaffure/quaffure.html