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
@article{arxiv.2412.10061,
title = {Quaffure: Real-Time Quasi-Static Neural Hair Simulation},
author = {Tuur Stuyck and Gene Wei-Chin Lin and Egor Larionov and Hsiao-yu Chen and Aljaz Bozic and Nikolaos Sarafianos and Doug Roble},
journal= {arXiv preprint arXiv:2412.10061},
year = {2025}
}