Quaffure: Real-Time Quasi-Static Neural Hair Simulation
Tuur Stuyck, Gene Wei-Chin Lin, Egor Larionov, Hsiao-yu Chen, Aljaz Bozic, Nikolaos Sarafianos, Doug Roble,
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.
| Paper | |
| Project Page | Webpage |
| Video | YouTube |
BibTeX
@inproceedings{stuyck2024quaffure,
title = {
Quaffure: Real-Time Quasi-Static Neural Hair Simulation
},
author = {
Stuyck, Tuur and
Lin, Gene Wei-Chin and
Larionov, Egor and
Chen, Hsiao-yu and
Bozic, Aljaz and
Sarafianos, Nikolaos and
Roble, Doug
},
booktitle = {
Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR)
},
month = {June},
year = {2025}
}