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Our ICLR 2022 paper: Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics

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Fine-grained-Differentiable-Physics-A-Yarn-level-Model-for-Fabrics

Our ICLR 2022 paper: Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics

Compiler and Dependencies

Simulation

The functions for simulations include:

  • SimHomo(): Simulate homogeneous cloth.
  • SimHeter(): Simulate blend woven cloth.
  • SimCollision(): A senario includes cloth-obstacle collision and cloth self-collision.

Training

The functions for training include:

  • TrainHeterFew(): Learn yarn's density, stretching stiffness, and bending stiffness.
  • TrainHeterFull(): Learn yarn's density, stretching stiffness, bending stiffness, shearing stiffness, and sliding friction coefficient.
  • TrainControl(): Learn the needed force to throw a piece of cloth in target place.

Training Data

The folder TrainingData contains simulated cloth dynamics for reproduce the training results shown in our paper.

  • Table_2 contains 5 by 5, 10 by 10, 17 by 17, and 25 by 25 plain cloth woven by Yarn-{1,2}.
  • 17x17_Cloth contains 17 by 17 plain, satin, and twill cloth woven by Yarn-{1,2}, Yarn-{1,3} and Yarn-{2,3}.

Notes

  1. The visual results are saved as a sequence of .obj files which can be viewed in Blender with Stop Motion OBJ plug-in.
  2. The simulation data, i.e positions and velocity, and training data are saved in hdf5 files which can be viewed in HDF View.
  3. Try to compile in Release or RelWithDebInfo model to avoid some possible errors.

Authors

Authors Deshan Gong, Zhanxing Zhu, Andy Bulpitt and He Wang

Deshan Gong, scdg@leeds.ac.uk

He Wang, he_wang@@ucl.ac.uk, Personal website

Project Webpage: http://drhewang.com/pages/diffcloth.html

Citation (Bibtex)

Please cite our paper if you find it useful:

@InProceedings{Gong_Fine_2022,
author={Deshan Gong, Zhanxing Zhu, Andy Bulpitt and He Wang},
booktitle={Proceedings of the International Confernece on Learning Representations},
title={Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics},
year={2022}}

License

Copyright (c) 2022, The University of Leeds, UK. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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