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DiffCloth

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Code repository for our paper DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

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최종 버전 다운로드 (.zip)

파일 수가 많아 일부만 표시됩니다. 전체 파일은 위 다운로드 버튼으로 확인해 주세요.

- - CMakeLists.txt
  - glfw3.lib
- - .gitignore
  - AUTHORS.md
  - example-quadratic.cpp
  - example-rosenbrock-box.cpp
  - example-rosenbrock-bracketing.cpp
  - example-rosenbrock-comparison.cpp
  - example-rosenbrock.cpp
  - LICENSE.md
  - README.md
- - .appveyor.yml
  - .DS_Store
  - .gitmodules
  - .travis.yml
  - CMakeLists.txt
  - CONTRIBUTING.rst
  - LICENSE.txt
  - README.rst
- CMakeLists.txt
- eigen
- pybind11
- dress.gif
- featured.png
- featuredaa.gif
- hat.gif
- teaser.jpeg
- test
- - backwardLog.txt
  - forwardLog.txt
.gitignore
.gitmodules
CMakeLists.txt
environment.yml
LICENSE
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/omegaiota/DiffCloth

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd DiffCloth

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. CMake

보통 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
CMake 빌드 설정을 생성하는 도구입니다.
C/C++ 컴파일러 Windows는 Visual Studio(Community 무료), macOS는 Xcode Command Line Tools, Linux는 gcc/g++ 패키지가 필요합니다.

cmake ..

소스코드를 분석해 빌드 설정 파일을 생성합니다 (build 폴더 안에서 실행해야 함).

✅ build 폴더 안에 실행 파일이 생성됐는지 확인하고, 직접 실행해보세요 (예: ./build/앱이름).

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# DiffCloth
Code repository for our paper [DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact](https://people.csail.mit.edu/liyifei/publication/diffcloth-differentiable-cloth-simulator/)
![](https://raw.githubusercontent.com/omegaiota/DiffCloth/main/gifs/teaser.jpeg)

[📃 Paper](https://people.csail.mit.edu/liyifei/uploads/diffcloth-highres-tog.pdf) | [🌍 Project](https://people.csail.mit.edu/liyifei/publication/diffcloth/)

### Tested Operating Systems
Ubuntu 22.04 | Mac OS 12

### 1. Download the repo:
**Make sure to use the `--recursive` option** to install the dependencies

`git clone --recursive https://github.com/omegaiota/DiffCloth.git`

### 2. Build CPP code with Cmake:
From the top directory:
```
mkdir build
cd build
cmake ..
make
```
### 3. Optimize/Visualize Section 6 Experiments:

* Run optimization:
    ```
    ./DiffCloth -demo {demooptions} -mode optimize -seed {randseed}
    ```

where `{demooptions}` is the name of the demos from the following options and `{randseed}` is an integer for random initialization of the initial guesses
    of the tasks.

The corresponding option for each of the experiments is:
    * 6.1 T-shirt:  `tshirt`
    * 6.1 Sphere: `sphere`
    * 6.2 Hat: `hat`
    * 6.2 Sock: `sock`
    * 6.3 Dress: `dress`

* Visualize optimization iters:
    ```
    ./DiffCloth -demo {demooptions} -mode visualize -exp {expName}
    ```

where `{expName}` is the iteration folder for visualization. The code repo comes with an example optimization run of T-shirt in `output/tshirt-exampleopt/`, and you can visualize the first iteration with

```
    ./DiffCloth -demo tshirt -mode visualize -exp tshirt-exampleopt/iter0/
    ```

The progress of the optimization is saved into the `output/` directory of the root folder. Intermediate progress are visualized using the custom written OpenGL viewer.

### 4. Build Python Binding and Run Hat Controller example:
Build Python Binding:

- Install anaconda for virtual environment.
- In project root folder, run `python setup.py install` to install the python binding package. Rerun this command if you modify the CPP code.
- Create conda virtual environment: `conda env create python=3.8 --file environment.yml`, and activate it through `conda activate diffcloth`

Train/Test Hat Controller example:
- Navigate to `src/python_code`
* Test pretrained network: run `python hatController.py --eval --render --load_expname 20210809-trainedBest`
* Train network: run `python hatController.py --render`
* Resume train: run `python hatController.py --train_resume --load_expname [expName] --load_epoch [epochNum]`

Simulations are saved to the `output/` directory of the root folder.

### Note
Feel free to contact me at liyifei@csail.mit.edu or create a Github issue if you have questions regarding setting up the repository, running examples or adding new examples.

### Citation
Please consider citing our paper if your find our research or this codebase helpful:

@article{Li2022diffcloth,
    author = {Li, Yifei and Du, Tao and Wu, Kui and Xu, Jie and Matusik, Wojciech},
    title = {DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact},
    year = {2022},
    issue_date = {February 2023},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {42},
    number = {1},
    issn = {0730-0301},
    url = {https://doi.org/10.1145/3527660},
    doi = {10.1145/3527660},
    abstract = {Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications. Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact&nbsp;[Ly et&nbsp;al. 2020]. We draw inspiration from previous work&nbsp;[Du et&nbsp;al. 2021] to propose a fast and novel method for deriving gradients in PD-based cloth simulation with dry frictional contact. Furthermore, we conduct a comprehensive analysis and evaluation of the usefulness of gradients in contact-rich cloth simulation. Finally, we demonstrate the efficacy of our simulator in a number of downstream applications, including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design, and real-to-sim transfer. We observe a substantial speedup obtained from using our gradient information in solving most of these applications.},
    journal = {ACM Trans. Graph.},
    month = {oct},
    articleno = {2},
    numpages = {20},
    keywords = {differentiable simulation, cloth simulation, Projective Dynamics}
    }

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