# Reconstructing Existing Levels through Level Inpainting

arXiv: https://arxiv.org/abs/2309.09472 · HTML: https://mlsanity.com/paper/2309.09472/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Coherent Semantic Attention for Image Inpainting](https://mlsanity.com/paper/1905.12384/) (2019)
- [Image inpainting: A review](https://mlsanity.com/paper/1909.06399/) (2019)
- [High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling](https://mlsanity.com/paper/2005.11742/) (2020)
- [Patch-Based Image Inpainting with Generative Adversarial Networks](https://mlsanity.com/paper/1803.07422/) (2018)
- [The VGLC: The Video Game Level Corpus](https://mlsanity.com/paper/1606.07487/) (2016)
- [Super Mario as a String: Platformer Level Generation Via LSTMs](https://mlsanity.com/paper/1603.00930/) (2016)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study](https://mlsanity.com/paper/2106.15877/) (2021)
- [Video Game Level Repair via Mixed Integer Linear Programming](https://mlsanity.com/paper/2010.06627/) (2020)
- [Automated Game Design via Conceptual Expansion](https://mlsanity.com/paper/1809.02232/) (2018)
- [Path of Destruction: Learning an Iterative Level Generator Using a Small Dataset](https://mlsanity.com/paper/2202.10184/) (2022)
- [Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders](https://mlsanity.com/paper/2006.09807/) (2020)
- [Game Level Clustering and Generation using Gaussian Mixture VAEs](https://mlsanity.com/paper/2009.09811/) (2020)
- [Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning](https://mlsanity.com/paper/2110.03181/) (2021)

## Built on by (most cited first, 1 of 1)

- [In the Blink of an Eye: Instant Game Map Editing using a Generative-AI Smart Brush](https://mlsanity.com/paper/2503.19793/) (2025)

## Abstract (arXiv)

Procedural Content Generation (PCG) and Procedural Content Generation via Machine Learning (PCGML) have been used in prior work for generating levels in various games. This paper introduces Content Augmentation and focuses on the subproblem of level inpainting, which involves reconstructing and extending video game levels. Drawing inspiration from image inpainting, we adapt two techniques from this domain to address our specific use case. We present two approaches for level inpainting: an Autoencoder and a U-net. Through a comprehensive case study, we demonstrate their superior performance compared to a baseline method and discuss their relative merits. Furthermore, we provide a practical demonstration of both approaches for the level inpainting task and offer insights into potential directions for future research.