Augmented Mass-Spring model for Real-Time Dense 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_ | 1866914090992009216 |
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| author | Herrera, Jorge Alejandro Amador Zhou, Yi Sun, Xin Shu, Zhixin He, Chengan Pirk, Sören Michels, Dominik L. |
| author_facet | Herrera, Jorge Alejandro Amador Zhou, Yi Sun, Xin Shu, Zhixin He, Chengan Pirk, Sören Michels, Dominik L. |
| contents | We propose a novel Augmented Mass-Spring (AMS) model for real-time simulation of dense hair at strand level. Our approach considers the traditional edge, bending, and torsional degrees of freedom in mass-spring systems, but incorporates an additional one-way biphasic coupling with a ghost rest-shape configuration. Trough multiple evaluation experiments with varied dynamical settings, we show that AMS improves the stability of the simulation in comparison to mass-spring discretizations, preserves global features, and enables the simulation of non-Hookean effects. Using an heptadiagonal decomposition of the resulting matrix, our approach provides the efficiency advantages of mass-spring systems over more complex constitutive hair models, while enabling a more robust simulation of multiple strand configurations. Finally, our results demonstrate that our framework enables the generation, complex interactivity, and editing of simulation-ready dense hair assets in real-time. More details can be found on our project page: https://agrosamad.github.io/AMS/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17144 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Augmented Mass-Spring model for Real-Time Dense Hair Simulation Herrera, Jorge Alejandro Amador Zhou, Yi Sun, Xin Shu, Zhixin He, Chengan Pirk, Sören Michels, Dominik L. Graphics We propose a novel Augmented Mass-Spring (AMS) model for real-time simulation of dense hair at strand level. Our approach considers the traditional edge, bending, and torsional degrees of freedom in mass-spring systems, but incorporates an additional one-way biphasic coupling with a ghost rest-shape configuration. Trough multiple evaluation experiments with varied dynamical settings, we show that AMS improves the stability of the simulation in comparison to mass-spring discretizations, preserves global features, and enables the simulation of non-Hookean effects. Using an heptadiagonal decomposition of the resulting matrix, our approach provides the efficiency advantages of mass-spring systems over more complex constitutive hair models, while enabling a more robust simulation of multiple strand configurations. Finally, our results demonstrate that our framework enables the generation, complex interactivity, and editing of simulation-ready dense hair assets in real-time. More details can be found on our project page: https://agrosamad.github.io/AMS/. |
| title | Augmented Mass-Spring model for Real-Time Dense Hair Simulation |
| topic | Graphics |
| url | https://arxiv.org/abs/2412.17144 |