HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916060726296576 |
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| author | Srivastava, Astitva Chen, Hsiao-Yu Goldade, Ryan Herholz, Philipp Jiang, Zhongshi Lin, Gene Wei-Chin Yang, Lingchen Sarafianos, Nikolaos Stuyck, Tuur Roble, Doug Sharma, Avinash Larionov, Egor |
| author_facet | Srivastava, Astitva Chen, Hsiao-Yu Goldade, Ryan Herholz, Philipp Jiang, Zhongshi Lin, Gene Wei-Chin Yang, Lingchen Sarafianos, Nikolaos Stuyck, Tuur Roble, Doug Sharma, Avinash Larionov, Egor |
| contents | Recent advances in garment simulation have brought high-quality results closer to real-time performance. Physics-based simulators can produce accurate motion, but remain too computationally expensive for interactive applications. In contrast, linear blend skinning is efficient, but cannot capture the complex dynamics of loose-fitting garments, often leading to unrealistic motion and visual artifacts. Neural methods offer a promising alternative, yet they still struggle to animate loose clothing plausibly under strict runtime constraints. We present a fast and physically plausible approach for dynamic garment simulation. Our method trains a reduced-space neural dynamics simulator composed of independent coarse- and fine-level components. At the coarse level, the garment is driven by a set of virtual bones integrated with a lightweight neural network. Fine-scale wrinkle details are then recovered using a trained convolutional neural map. By decoupling identity-specific computation from real-time neural integration, our architecture maintains high performance while supporting diverse body shapes and motions. We further introduce an effective physics-supervision scheme that enables accurate results without relying on an external simulator. Experiments show that our method produces physically plausible garment dynamics, generalizes across a range of motions and body shapes, and supports a fixed set of garments. Our simulator runs at 300+ FPS on a commodity GPU, making it suitable for real-time applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20460 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning Srivastava, Astitva Chen, Hsiao-Yu Goldade, Ryan Herholz, Philipp Jiang, Zhongshi Lin, Gene Wei-Chin Yang, Lingchen Sarafianos, Nikolaos Stuyck, Tuur Roble, Doug Sharma, Avinash Larionov, Egor Graphics Computer Vision and Pattern Recognition Recent advances in garment simulation have brought high-quality results closer to real-time performance. Physics-based simulators can produce accurate motion, but remain too computationally expensive for interactive applications. In contrast, linear blend skinning is efficient, but cannot capture the complex dynamics of loose-fitting garments, often leading to unrealistic motion and visual artifacts. Neural methods offer a promising alternative, yet they still struggle to animate loose clothing plausibly under strict runtime constraints. We present a fast and physically plausible approach for dynamic garment simulation. Our method trains a reduced-space neural dynamics simulator composed of independent coarse- and fine-level components. At the coarse level, the garment is driven by a set of virtual bones integrated with a lightweight neural network. Fine-scale wrinkle details are then recovered using a trained convolutional neural map. By decoupling identity-specific computation from real-time neural integration, our architecture maintains high performance while supporting diverse body shapes and motions. We further introduce an effective physics-supervision scheme that enables accurate results without relying on an external simulator. Experiments show that our method produces physically plausible garment dynamics, generalizes across a range of motions and body shapes, and supports a fixed set of garments. Our simulator runs at 300+ FPS on a commodity GPU, making it suitable for real-time applications. |
| title | HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.20460 |