Reconstructing Existing Levels through Level Inpainting
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
18 of 33 references.
References · 18
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
- 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
- 2019Coherent Semantic Attention for Image Inpaintingcited by 97
- 2019Image inpainting: A reviewcited by 89
- 2020High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsamplingcited by 79
- 2018Patch-Based Image Inpainting with Generative Adversarial Networkscited by 70
- 2016The VGLC: The Video Game Level Corpuscited by 41
- 2016Super Mario as a String: Platformer Level Generation Via LSTMscited by 40
- 2021Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencodercited by 27
- 2021Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Studycited by 25
6 more
- 2020Video Game Level Repair via Mixed Integer Linear Programmingcited by 18
- 2018Automated Game Design via Conceptual Expansioncited by 15
- 2022Path of Destruction: Learning an Iterative Level Generator Using a Small Datasetcited by 14
- 2020Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoderscited by 11
- 2020Game Level Clustering and Generation using Gaussian Mixture VAEscited by 9
- 2021Tile Embedding: A General Representation for Procedural Level Generation via Machine Learningcited by 9
Built on by
Citing papers per year: 2025: 1.
1 paper.