Multi-pass-GAN

Public source code for the SCA paper "A Multi-Pass GAN for Fluid Flow Super-Resolution". Authors: Maximilian Werhahn, You Xie, MengYu Chu, Nils Thuerey. Technical University of Munich.

Paper: https://arxiv.org/pdf/1906.01689.pdf,
Video: https://www.youtube.com/watch?v=__WE22dB6AA

Requirements

tensorflow >= 1.10
mantaflow for datagen

Directories

../datagen/: data generation via mantaflow
../GAN/: output + training + network files
../tools_wscale/: helper functions, data loader, etc.

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".

Data Generation

Either use the file ../datagen/gen_mul_data.py or similar commands for the file ../datagen/gen_sim_grow_slices_data.py to generate a dataset. It will be stored in ../data3d_growing/sim_%04d.

Training

Call ../GAN/example_run_training.py

Applying models

Call ../GAN/example_run_output.py, pretrained models can be found in the branch "models"