by Yitong Deng, Hong-Xing Yu, Diyang Zhang, Jiajun Wu, and Bo Zhu.
Our paper and video results can be found at our project website.
Our code is tested on Windows 11 with CUDA 11.8, Python 3.10.9, PyTorch 2.0.1, and Taichi 1.6.0.
To set up the environment, first create a conda environment:
conda create -n "nfm_env" python=3.10.9 ipython
conda activate nfm_envThen, install PyTorch with:
python -m pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118Finally, install the requirements with:
pip install -r requirements.txtFor running simulation, simply execute:
python run.pyHyperparameters can be tuned by changing the values in the file hyperparameters.py. Checkpointing is available by setting the from_frame variable to the desired frame, given that the checkpoint of that frame can be found in logs/[exp_name]/ckpts.
The results will be stored in logs/[exp_name]/vtks. We recommend using ParaView to load these .vti files as a sequence and visualize them by selecting Volume in the Representation drop-down menu.
If you find our paper or code helpful, consider citing:
@article{deng2023neural,
title={Fluid Simulation on Neural Flow Maps},
author={Yitong Deng and Hong-Xing Yu and Diyang Zhang and Jiajun Wu and Bo Zhu},
journal={ACM Trans. Graph.},
volume={42},
number={6},
article={},
year={2023},
}