tempoGAN

Here you can find the source code for the SIGGRAPH paper "tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow".

Further information: https://ge.in.tum.de/publications/tempoGAN/

Getting started

This is a brief overview and getting-started guide for the source code of the tempoGAN project. It can also be found under tempoGAN/tensorflow/tempoGAN/README.txt.

Software

Note: tensorflow 1.3 or higher is required to run.

Directories

Main source code directories:

.../tensorflow/datagen: scene files for generating 2D/3D training data

.../tensorflow/tools: contains necessary tools for inputs, outputs, neural networks operation, and etc.

.../tensorflow/GAN: contains the tempoGAN model.

And two data directories were ouputs will be written:

.../tensorflow/2ddata_sim: contains the training, validation and test data

.../tensorflow/2ddata_gan: outputs will be written here

Compilation

First, compile mantaflow with numpy support (as usual), follow http://mantaflow.com/install.html. One difference is, in the CMake settings, numpy shoule be enabled: "cmake .. -DGUI=ON -DOPENMP=ON -DNUMPY=ON". Note that if mantaflow is installed on a remote server, GUI is not supported, i.e.: "cmake .. -DGUI=OFF -DOPENMP=ON -DNUMPY=ON".

All of the following scripts assume that you execute them from the mantaflow/tensorflow/tempoGAN/ directory (they often use relative paths).

Data Generation

Then generate simulation data with the following command, e.g.:

manta ../datagen/gen_sim_data.py basePath ../2ddata_sim/ reset 1 savenpz 1

You can add "gui 0" on the command line to hide the UI and speed up the data generation runs. Also generate the sample plume data (gen_sim_2006.py for 2D, gen_sim_3006.py for 3D) into the 2ddata_sim directory.

Training

Then you can start to train a GAN using:

python example_run_training.py

This trains four models, for a quick test disable the later three. These example only use 2 simulations as training data. To train proper models, we recommend ca. 200 frames of input from at least 10 sims.

Applying the Model

After you trained a GAN model, you can use the model to generate new outputs:

python example_run_output.py

By default, these examples run on simulation "2006" and "3006" for 3D.

Note: all the commands above are just examples, please check parameters when running them (esp. paths, simulation ID ranges etc.). The code currently uses .uni files for reading and writing grids. This is the default file format of mantaflow, so you can check out the sources at http://mantaflow.com/ for details on how to read and write these files.

BibTex citation

If you find this work useful, please consider citing the corresponding paper:

@article{xie2018tempoGAN,
    title={tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow},
    author={Xie, You and Franz,Erik and Chu, Mengyu and Thuerey, Nils},
    journal={ACM Transactions on Graphics (TOG)},
    volume={37},
    number={4},
    pages={95},
    year={2018},
    publisher={ACM}
}

pigcage / tempoGAN