# World-GAN: a Generative Model for Minecraft Worlds

arXiv: https://arxiv.org/abs/2106.10155 · HTML: https://mlsanity.com/paper/2106.10155/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020)
- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Growing 3D Artefacts and Functional Machines with Neural Cellular Automata](https://mlsanity.com/paper/2103.08737/) (2021)
- [Minimum-Distortion Embedding](https://mlsanity.com/paper/2103.02559/) (2021)
- [EvoCraft: A New Challenge for Open-Endedness](https://mlsanity.com/paper/2012.04751/) (2020)
- [Generative Design in Minecraft (GDMC), Settlement Generation Competition](https://mlsanity.com/paper/1803.09853/) (2018)
- [TOAD-GAN: Coherent Style Level Generation from a Single Example](https://mlsanity.com/paper/2008.01531/) (2020)
- [Tile Pattern KL-Divergence for Analysing and Evolving Game Levels](https://mlsanity.com/paper/1905.05077/) (2019)
- [DOOM Level Generation using Generative Adversarial Networks](https://mlsanity.com/paper/1804.09154/) (2018)
- [Organic Building Generation in Minecraft](https://mlsanity.com/paper/1906.05094/) (2019)
- [The AI Settlement Generation Challenge in Minecraft: First Year Report](https://mlsanity.com/paper/2103.14950/) (2021)
- [Generative Design in Minecraft: Chronicle Challenge](https://mlsanity.com/paper/1905.05888/) (2019)
- [Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda](https://mlsanity.com/paper/2001.05065/) (2020)
- [Entity Embedding as Game Representation](https://mlsanity.com/paper/2010.01685/) (2020)
- [Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning](https://mlsanity.com/paper/2005.12579/) (2020)

## Built on by (most cited first, 14 of 14)

- [WorldEval: World Model as Real-World Robot Policies Evaluator](https://mlsanity.com/paper/2505.19017/) (2025)
- [Creative Agents: Empowering Agents with Imagination for Creative Tasks](https://mlsanity.com/paper/2312.02519/) (2023)
- [DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraft](https://mlsanity.com/paper/2404.15538/) (2024)
- [Open-Ended Evolution for Minecraft Building Generation](https://mlsanity.com/paper/2209.03108/) (2022)
- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [Evolving Flying Machines in Minecraft Using Quality Diversity](https://mlsanity.com/paper/2302.00782/) (2023)
- [Balancing of competitive two-player Game Levels with Reinforcement Learning](https://mlsanity.com/paper/2306.04429/) (2023)
- [3D Building Generation in Minecraft via Large Language Models](https://mlsanity.com/paper/2406.08751/) (2024)
- [Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges](https://mlsanity.com/paper/2508.09561/) (2025)
- [Generating Redstone Style Cities in Minecraft](https://mlsanity.com/paper/2307.09777/) (2023)
- [Scaffold Diffusion: Sparse Multi-Category Voxel Structure Generation with Discrete Diffusion](https://mlsanity.com/paper/2509.00062/) (2025)
- [Markov Senior -- Learning Markov Junior Grammars to Generate User-specified Content](https://mlsanity.com/paper/2408.05959/) (2024)
- [Text-to-Level Diffusion Models With Various Text Encoders for Super Mario Bros](https://mlsanity.com/paper/2507.00184/) (2025)
- [Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes](https://mlsanity.com/paper/2604.22847/) (2026)

## Abstract (arXiv)

This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator. Our method is motivated by the dense representations used in Natural Language Processing (NLP) introduced with word2vec [1]. The proposed block2vec representations make World-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. Finally, we demonstrate that changing this new representation space allows us to change the generated style of an already trained generator. World-GAN enables its users to generate Minecraft worlds based on parts of their creations.