Quaffure: Real-Time Quasi-Static Neural Hair Simulation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915237471453184 |
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| author | Stuyck, Tuur Lin, Gene Wei-Chin Larionov, Egor Chen, Hsiao-yu Bozic, Aljaz Sarafianos, Nikolaos Roble, Doug |
| author_facet | Stuyck, Tuur Lin, Gene Wei-Chin Larionov, Egor Chen, Hsiao-yu Bozic, Aljaz Sarafianos, Nikolaos Roble, Doug |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10061 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quaffure: Real-Time Quasi-Static Neural Hair Simulation Stuyck, Tuur Lin, Gene Wei-Chin Larionov, Egor Chen, Hsiao-yu Bozic, Aljaz Sarafianos, Nikolaos Roble, Doug Computer Vision and Pattern Recognition Graphics 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 |
| title | Quaffure: Real-Time Quasi-Static Neural Hair Simulation |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2412.10061 |