Inferring Hybrid Neural Fluid Fields from Videos

This is the official code for Inferring Hybrid Neural Fluid Fields from Videos (NeurIPS 2023).

[Paper] [Project Page]

Installation

Install with conda:

conda env create -f environment.yml
conda activate hyfluid

or with pip:

pip install -r requirements.txt

Data

The demo data is available at data/ScalarReal. The full ScalarFlow dataset can be downloaded here.

Quick Start

To learn the hybrid neural fluid fields from the demo data, firstly reconstruct the density field by running (~40min):

bash scripts/train.sh

Then, reconstruct the velocity field by jointly training with the density field (~15 hours on a single A6000 GPU.):

bash scripts/train_j.sh

Finally, add vortex particles and optimize their physical parameters (~40min):

bash scripts/train_vort.sh

The results will be saved in ./logs/exp_real. With the learned hybrid neural fluid fields, you can re-simulate the fluid by using the velocity fields to advect density:

bash scripts/test_resim.sh

Or, you can predict the future states by extrapolating the velocity fields:

bash scripts/test_future_pred.sh

Citation

If you find this code useful for your research, please cite our paper:

@article{yu2023inferring,
  title={Inferring Hybrid Neural Fluid Fields from Videos},
  author={Yu, Hong-Xing and Zheng, Yang and Gao, Yuan and Deng, Yitong and Zhu, Bo and Wu, Jiajun},
  journal={NeurIPS},
  year={2023}
}