A Cross-Platform Modern C++20 Library of Unified Incremental Potential Contact.
Both C++ and Python API are provided!
Website: ➡️ https://spirimirror.github.io/libuipc-doc/
Libuipc is a library that offers a unified GPU incremental potential contact framework for simulating the dynamics of rigid bodies, soft bodies, cloth, and threads, and their couplings. It ensures accurate, penetration-free frictional contact and is naturally differentiable. Libuipc aims to provide robust and efficient forward and backward simulations, making it easy for users to integrate with machine learning frameworks, inverse dynamics, robotics, and more.
We are actively developing Libuipc and will continue to add more features and improve its performance. We welcome any feedback and contributions from the community!
- Easy & Powerful: Libuipc offers an intuitive and unified approach to creating and accessing vivid simulation scenes, supporting a variety of objects and constraints that can be easily added.
- Fast & Robust: Libuipc is designed to run fully in parallel on the GPU, achieving high performance and enabling large-scale simulations. It features a robust and accurate frictional contact model that effectively handles complex frictional scenarios without penetration.
- High Flexibility: Libuipc provides APIs in both Python and C++ and supports both Linux and Windows systems.
- Fully Differentiable: Libuipc provides differentiable simulation APIs for backward optimizations. (Coming Soon)
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- Finite Element-Based Deformable Simulation
- Rigid & Soft Body Strong Coupling Simulation
- Penetration-Free & Accurate Frictional Contact Handling
- User Scriptable Animation Control
- Fully Differentiable Simulation (Diff-Sim Coming Soon)
2024-11-25: Libuipc v0.9.0 (Alpha) is published! We are excited to share our work with the community. This is a preview version, if you have any feedback or suggestions, please feel free to contact us! Issues and PRs are welcome!
If you use Libuipc in your project, please cite our works:
@misc{huang2024advancinggpuipcstiff,
title={Advancing GPU IPC for stiff affine-deformable simulation},
author={Kemeng Huang and Xinyu Lu and Huancheng Lin and Taku Komura and Minchen Li},
year={2024},
eprint={2411.06224},
archivePrefix={arXiv},
primaryClass={cs.GR},
url={https://arxiv.org/abs/2411.06224},
}
@article{gipc2024,
author = {Huang, Kemeng and Chitalu, Floyd M. and Lin, Huancheng and Komura, Taku},
title = {GIPC: Fast and Stable Gauss-Newton Optimization of IPC Barrier Energy},
year = {2024},
publisher = {Association for Computing Machinery},
volume = {43},
number = {2},
issn = {0730-0301},
doi = {10.1145/3643028},
journal = {ACM Trans. Graph.},
month = {mar},
articleno = {23},
numpages = {18}
}