An implemention of paper MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters.
Yusen Feng, Xiyan Xu, Libin Liu
Project Page: https://pku-mocca.github.io/MuscleVAE-page/
Install the packages listed in requirements.txt. This repo can be run on both Linux and Windows. But the render part is only supported on Windows. As of now, we only offer a basic OpenGL-based test render that can roughly display muscles and geometries, and it only supports the Windows operating system.
conda create -n musclevae python=3.8
conda activate musclevae
pip install cython==0.29.30 tqdm scipy setuptools tensorboard tensorboardx psutil pyyaml cmake opt_einsum panda3d chardet trimesh
conda install mpi4py
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
Please to make sure the cython is the version 0.29.** or 0.28.**, otherwise you may encounter some errors.
Then install the package ModifyODESrc(Cpp) and MuscleVAE(Python) in this directory. You should change into the folder of this project, and run
cd ModifyODESrc/
pip install -e .
cd ..
pip install -e .
-e means installing the package in "editable" mode. The package will change automatically without a reinstall after you make changes to the Python source files. But be careful and reinstall the package when you change the Cython files and type the sh ./clear.sh or .\clear.cmd and then reinstall when you change the Cpp files.
For the motion data, we use run, walk and jump motion from the LaFAN1 dataset. We offer the processed data in the following link. Please download it into Data/ReferenceData/binary_data/.
https://drive.google.com/file/d/1b0iNcuDOpwi62Mu7Xn9j86obEvgs3QKb/view?usp=sharing
We also offer the BVH format in Data/ReferenceData/walk_run_jump/, you can use the following command to convert the BVH format to binary format.
python ./Script/build_motion_dataset.py
It need an .yml file, such as ./Data/Parameters/muscle.yml.
We offer a pretrained model and configuration file in the following link. Please download it into Data/NNModel.
# low-level
https://drive.google.com/drive/folders/1Pd2zqtSflfgOYPJYUoq7DJPLfmzqsboS?usp=sharing
# high-level
https://drive.google.com/drive/folders/1UZZDs5-cvVrKEOf8NSMOmHmbKyrUIqAl?usp=sharing
We offer three tasks now: tracking (low-level), random generation (low-level) and velocity control (high-level). All of them are in in the folder PlayGround and can be played by directly run the code. For more details, you can refer to the PlayGround/random_generation.py, PlayGround/track_something.py and PlayGround/velocity_control.py. As for the tracking task, we offer a code to visualize the world model in the position of reference character, which is in PlayGround/visualize_world_model.py. After typing the following command, you should select the configuration file .yml first, then the checkpoint .data.
python ./PlayGround/track_something.py --start_frame 10
python ./PlayGround/random_generation.py
python ./PlayGround/velocity_control.py
python ./PlayGround/visualize_world_model.py --start_frame 6000
Till now, we only offer the playing codes. The training codes will come soon.
If you find our work useful in your research, please consider citing:
@inproceedings{feng2023musclevae,
author = {Feng, Yusen and Xu, Xiyan and Liu, Libin},
title = {MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters},
year = {2023},
isbn = {9798400703157},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3610548.3618137},
doi = {10.1145/3610548.3618137},
booktitle = {SIGGRAPH Asia 2023 Conference Papers},
articleno = {3},
numpages = {11},
series = {SA '23}
}
The code is developed based open-source structure of ControlVAE, and if you find world model is helpful for your research, please consider citing:
@article{Yao_2022,
title={ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters},
volume={41},
ISSN={1557-7368},
url={http://dx.doi.org/10.1145/3550454.3555434},
DOI={10.1145/3550454.3555434},
number={6},
journal={ACM Transactions on Graphics},
publisher={Association for Computing Machinery (ACM)},
author={Yao, Heyuan and Song, Zhenhua and Chen, Baoquan and Liu, Libin},
year={2022},
month=nov, pages={1–16} }