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Source code for the SIGGRAPH paper "tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow"

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tempoGAN

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.)

BibTex citation

@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}
}

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Source code for the SIGGRAPH paper "tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow"

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