This is the code base for our SIGGRAPH Asia 2023 paper. We develop our method based on the open-source library SPlisHSPlasH and we sincerely thank the effort of its authors.
We have built and tested the code on Ubuntu22.04.
Required infrastructures can be installed via:
sudo apt install git cmake xorg-dev freeglut3-dev build-essential libomp-dev
Required python packages can be directly installed by pip install -r requirements.txt.
First shallow clone this repo with git:
git clone --depth=5 git@github.com:zhehaoli1999/DiffFR.git
Then we recommend to create a new conda environment (such as "diffFR") and activate it:
conda create --name diffFR python=3.7
conda activate diffFR
Then find build.sh under the root dir of the project, and use commands like bash build.sh to execuate this shell (please ensure your
shell has already execuated conda init). It will use cmake to configure the
project and then compile it with your compiler.
You can find all raw data records and the scripts for figure plotting of our experiments in the experiments folder.
The simulation scene settings are described as json files, and the initial setting of velocities and positions of fluid and rigid particles are stored as bgeo and binary files.
To run each task, you need to input a scene json file, as well as an optional binary file of the initial settings of the fluid environment (without specification, all fluid particles will have the positions described in the json file with zero velocities. )
| Task | Scene json file | state file | taskType | design variables | notes |
|---|---|---|---|---|---|
| water rafting | diff-water-rafting-bunny.json | water_rafting/state_130.bin | 'water-rafting' | vx, vz omega | load-fluid-pos-and-vel |
| high diving | diff-high-diving-duck.json | high_diving/state_98.bin | 'high-diving' | omega | load-fluid-pos |
| stone skipping | diff-stone-skipping.json | stone_skipping/state_18.bin | 'stone-skipping' | v, omega | load-fluid-pos |
| bottle flipping (stage 1) | diff-bottle-model.json | bottle_flip/state_54.bin | 'bottle-flip' | v, omega | load-fluid-pos |
| bottle flipping (stage 2) | diff-bottle-model-collide.json | bottle_flip/state_54.bin | 'bottle-flip' | v, omega | load-fluid-pos |
| on-water billiards | billiards-on-water-2balls.json | billiards/state_17.bin | - | v, omega | use diff-rigid-contact-multi.py, load-fluid-pos |
First, cd ./experiments/rigid_body_trajectory_optimization/python.
For gradient-based optimization (ours), use gradient-based-optimize.py. For gradient-free optimization (CMA-ES & 1+1 ES), use opt-ng.py.
- bottle flipping
python gradient-based-optimize.py --scene=../scene/diff-bottle-model.json --state=../state/bottle_flip/state_54.bin --load-fluid-pos --taskType=bottle-flip
- stone skipping
python gradient-based-optimize.py --scene=../scene/diff-stone-skipping.json --state=../state/stone_skipping/state_18.bin --load-fluid-pos --taskType=stone-skipping
- water rafting
python gradient-based-optimize.py --scene=../scene/diff-water-rafting-bunny.json --state=../state/water_rafting/state_130 --load-fluid-pos-and-vel --taskType=water-rafting
- high diving
python gradient-based-optimize.py --scene=../scene/diff-high-diving-duck.json --state=../state/high_diving/state_98.bin --load-fluid-pos --taskType=high-diving
- on-water billiards
python diff-rigid-contact-multi.py --scene=../scene/billiards-on-water-2balls.json --state=../state/billiards/state_17.bin --load-fluid-pos
First, cd ./experiments/bottle_flip_robot/python
python bottle-flip-robot.py --scene=../scene/diff-bottle-model-360degree.json --state=../state/state_1.365999.bin --load-fluid-pos
First, cd ./experiments/on_water_inverted_pendulum/python
Train:
python cartpole-diff-controller.py --scene=../scene/cartpole-diff-controller.json --state=../state/state_0.879992.bin --load-fluid-pos
Test:
python cartpole-diff-controller.py --scene=../scene/cartpole-diff-controller.json --state=../state/state_0.879992.bin --load-fluid-pos --test=../saved_models/success8s-2023-01-14-23-07-05-cartpole-epoch6.pth
Please see the content of ./experiments/others for code and raw data.
For postprocess we use Houdini to reconstruct the fluid surface mesh from particles, and use Blender Cycles for rendering.
If you have any question, please feel free to open an issue or email zhehaoli@mail.ustc.edu.cn.
To cite our paper, here is the bibtex:
@article{10.1145/3618318,
author = {Li, Zhehao and Xu, Qingyu and Ye, Xiaohan and Ren, Bo and Liu, Ligang},
title = {DiffFR: Differentiable SPH-Based Fluid-Rigid Coupling for Rigid Body Control},
year = {2023},
issue_date = {December 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {42},
number = {6},
issn = {0730-0301},
url = {https://doi.org/10.1145/3618318},
doi = {10.1145/3618318},
journal = {ACM Trans. Graph.},
articleno = {179},
numpages = {17}
}
MIT License