If you are using this code in your own project, please cite our paper:

@inproceedings{awiszus2021worldgan,
  title={World-GAN: a Generative Model for Minecraft Worlds},
  author={Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
  booktitle={Proceedings of the IEEE Conference on Games},
  year={2021}
}

World-GAN

Official pytorch implementation of the paper: "World-GAN: a Generative Model for Minecraft Worlds" For more information on TOAD-GAN, please refer to the paper (arxiv).

This Project includes graphics from the game Minecraft It is not affiliated with or endorsed by Mojang. The project was built for research purposes only.

CoG 2021

Our paper "World-GAN: a Generative Model for Minecraft Worlds" was accepted at CoG 2021!

Transaction on Games 2022

An extended Version of this paper has been published on the IEEE Transactions on Games. The additional code made for that publication can be found in the bert-extended branch. If you have questions regarding that, please do not hesitate to contact us!

Getting Started

This section includes the necessary steps to train World-GAN on your system.

Python

You will need Python 3 and the packages specified in requirements.txt. We recommend setting up a virtual environment with pip and installing the packages there.

NOTE: If you have a GPU, check if it is usable with Pytorch and change the requirement in the file to use +gpu instead of +cpu. Training on a GPU is significantly faster.

Install packages with:

$ pip3 install -r requirements.txt -f "https://download.pytorch.org/whl/torch_stable.html"

Make sure you use the pip3 that belongs to your previously defined virtual environment.

World-GAN

Training

Once all prerequisites are installed, World-GAN can be trained by running main.py. Make sure you are using the python installation you installed the prerequisites into.

There are several command line options available for training. These are defined in config.py. An example call which will train a 3-layer World-GAN TODO with 4000 iterations each scale would be:

$ python main.py *TODO* --alpha 100 --niter 4000 --nfc 64

Generating samples

If you want to use your trained World-GAN to generate more samples, use generate_samples.py. Make sure you define the path to a pretrained World-GAN and the correct input parameters it was trained with.

$ python generate_samples.py  --out_ path/to/pretrained/World-GAN --input-dir input --input-name lvl_1-1.txt --num_layer 3 --alpha 100 --niter 4000 --nfc 64

Experiments

We supply the code for experiments made for our paper. These files come with their own command line parameters each to control the experiment. Check the files for more info.

Built With

• Pytorch - Deep Learning Framework
• PyAnvilEditor - a framework for reading and writing nbt files for Minecraft 1.16

Authors

• Maren Awiszus - Institut für Informationsverarbeitung, Leibniz University Hanover
• Frederik Schubert - Institut für Informationsverarbeitung, Leibniz University Hanover

Copyright

This code is not endorsed by Mojang and is only intended for research purposes.