# TOAD-GAN: Coherent Style Level Generation from a Single Example

arXiv: https://arxiv.org/abs/2008.01531 · HTML: https://mlsanity.com/paper/2008.01531/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 22 of 22)

- [Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection](https://mlsanity.com/paper/2110.02855/) (2021)
- [Contextualize Me -- The Case for Context in Reinforcement Learning](https://mlsanity.com/paper/2202.04500/) (2022)
- [CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning](https://mlsanity.com/paper/2110.02102/) (2021)
- [Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks](https://mlsanity.com/paper/2101.07868/) (2021)
- [World-GAN: a Generative Model for Minecraft Worlds](https://mlsanity.com/paper/2106.10155/) (2021)
- [DreamGarden: A Designer Assistant for Growing Games from a Single Prompt](https://mlsanity.com/paper/2410.01791/) (2024)
- [RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network](https://mlsanity.com/paper/2206.14098/) (2022)
- [Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry](https://mlsanity.com/paper/2206.00106/) (2022)
- [Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations](https://mlsanity.com/paper/2105.12960/) (2021)
- [ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing](https://mlsanity.com/paper/2202.11616/) (2022)
- [Hyperparameters in Contextual RL are Highly Situational](https://mlsanity.com/paper/2212.10876/) (2022)
- [Balancing of competitive two-player Game Levels with Reinforcement Learning](https://mlsanity.com/paper/2306.04429/) (2023)
- [G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning](https://mlsanity.com/paper/2407.10483/) (2024)
- [SinTra: Learning an inspiration model from a single multi-track music segment](https://mlsanity.com/paper/2204.09917/) (2022)
- [BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning](https://mlsanity.com/paper/2212.01589/) (2022)
- [Ensemble Learning For Mega Man Level Generation](https://mlsanity.com/paper/2107.12524/) (2021)
- [Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach](https://mlsanity.com/paper/2309.13071/) (2023)
- [Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models](https://mlsanity.com/paper/2410.23108/) (2024)
- [Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning](https://mlsanity.com/paper/2107.02052/) (2021)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)
- [Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning](https://mlsanity.com/paper/2503.18748/) (2025)
- [Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning](https://mlsanity.com/paper/2509.24341/) (2025)

## Abstract (arXiv)

In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.