XRTailor is a GPU-accelerated cloth simulation engine optimized for large-scale data generation. By leveraging parallel computing techniques, XRTailor delivers high-fidelity cloth dynamics while maintaining performance, making it a practical choice for applications in animation, gaming and machine learning dataset synthesis.
Collisions. Collision detection and response are essential for cloth simulation. XRTailor supports obstacle-cloth collision, environment-cloth collision and self-collision. These features help maintain natural interactions between cloth and surrounding objects.
Fully Parallelized. To achieve better performance, XRTailor employs advanced data structures and algorithms specifically designed for GPU execution. By maximizing parallelism, the engine supports rapid computation, making it suitable for real-time and offline simulations alike.
Balanced Performance Modes. XRTailor offers two modes to accommodate different needs:
Swift Mode: Optimized for real-time applications, offering rapid simulations with simplified fabric properties and collision handling.
Quality Mode: Prioritizes accuracy, delivering highly detailed simulations at the cost of increased computational overhead.
Fully Automatic. Unlike existing cloth simulators, animators do not need to place the cloth pieces in appropriate positions to dress an avatar.
Highly Compatible with SMPL(X). XRTailor supports SMPL, SMPLH, SMPLX with AMASS integration.
Multi Platform Support. XRTailor runs on Windows and Linux systems that support CUDA, offering flexibility across computing environments.
OpenGL Rendering. A built-in graphical interface provides visualization and control over the simulation process.
Please refer to our documentation page for more details.
XRTailor is an open source project that is contributed by researchers and engineers from both the academia and the industry. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
The license of our codebase is Apache-2.0, see LICENSE for more information. Note that XRTailor is developed upon other open-source projects and uses many third-party libraries. Refer to docs/licenses to view the full licenses list. We would like to pay tribute to open-source implementations to which we rely on.
If you find this project useful in your research, please consider cite:
@misc{xrtailor,
title={OpenXRLab GPU Cloth Simulator},
author={XRTailor Contributors},
howpublished = {\url{https://github.com/openxrlab/xrtailor}},
year={2025}
}
We appreciate all contributions to improve XRTailor. Please refer to CONTRIBUTING.md for the contributing guideline.
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[27] SMPL: A Skinned Multi-Person Linear Model. Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, Michael J. Black. 2015
[28] Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. Federica Bogo*, Angjoo Kanazawa*, Christoph Lassner, Peter Gehler, Javier Romero, Michael Black. 2016
[29] Embodied Hands: Modeling and Capturing Hands and Bodies Together. Javier Romero*, Dimitrios Tzionas*, and Michael J Black. 2017
[30] Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. G. Pavlakos*, V. Choutas*, N. Ghorbani, T. Bolkart, A. A. A. Osman, D. Tzionas and M. J. Black. 2019
[31] AMASS: Archive of Motion Capture as Surface Shapes. Mahmood, Naureen and Ghorbani, Nima and Troje, Nikolaus F. and Pons-Moll, Gerard and Black, Michael J.
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