This is the PyTorch implementation of the color-to-voxel model translation presented on ECCV 2018.

The code is based on the PyTorch implementation of the pix2pix and CycleGAN.

If you use this code for your research, please cite:

@InProceedings{Kniaz2018,
author="Kniaz, Vladimir A. and
Knyaz, Vladimir V. and Fabio Remondino,
title={Image-to-Voxel Model Translation with Conditional Adversarial Networks},
booktitle={{Computer Vision -- ECCV 2018 Workshops",
year="2018}},
publisher={Springer International Publishing},
}

• Linux or macOS
• Python 2 or 3
• CPU or NVIDIA GPU + CUDA CuDNN

• Install PyTorch and dependencies from http://pytorch.org
• Install Torch vision from the source.

git clone https://github.com/pytorch/vision
cd vision
python setup.py install

• Install python libraries visdom and dominate.

pip install visdom
pip install dominate

• Clone this repo:

git clone https://github.com/vlkniaz/Z_GAN

• Go to the repo directory

cd Z_GAN

• Download a Z-GAN dataset:

bash ./datasets/download_zgan_dataset.sh mini

• Train a model:

bash scripts/train_zgan.sh

• To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/thermal_gan_rel/web/index.html
• Test the model:

bash scripts/test_zgan.sh

The test results will be saved to a html file here: ./results/z_gan/test_latest/index.html.

Download a pre-trained model with ./pretrained_models/download_zgan_model.sh.

• For example, if you would like to download Z-GAN model on the mini dataset,

bash pretrained_models/download_zgan_model.sh Z_GAN

• Download the mini datasets

bash ./datasets/download_zgan_dataset.sh mini

• Then generate the results using

bash scripts/test_zgan_pretrained.sh

• The test results will be saved to a html file here: ./results/Z_GAN_pretrained/test_latest/index.html.