tile2tile: Learning Game Filters for Platformer Style Transfer
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Abstract (arXiv)
We present tile2tile, an approach for style transfer between levels of tile-based platformer games. Our method involves training models that translate levels from a lower-resolution sketch representation based on tile affordances to the original tile representation for a given game. This enables these models, which we refer to as filters, to translate level sketches into the style of a specific game. Moreover, by converting a level of one game into sketch form and then translating the resulting sketch into the tiles of another game, we obtain a method of style transfer between two games. We use Markov random fields and autoencoders for learning the game filters and apply them to demonstrate style transfer between levels of Super Mario Bros, Kid Icarus, Mega Man and Metroid.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 15 Aug 2022.
Builds on
14 of 29 references.
References · 14
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2017Neural Style Transfer: A Reviewcited by 199
- 2017Procedural Content Generation via Machine Learning (PCGML)cited by 136
- 2016The VGLC: The Video Game Level Corpuscited by 41
- 2016Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworkscited by 22
- 2020Conditional Level Generation and Game Blendingcited by 17
- 2019Tile Pattern KL-Divergence for Analysing and Evolving Game Levelscited by 16
- 2018Automated Game Design via Conceptual Expansioncited by 15
- 2020Controllable Level Blending between Games using Variational Autoencoderscited by 14
- 2020Exploring Level Blending across Platformers via Paths and Affordancescited by 14
- 2020Towards Game Design via Creative Machine Learning (GDCML)cited by 12
- 2020Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoderscited by 11
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.
- 2026Evolutionary Transfer Learning for Dragonchess16 Mar 2026
- 2023Procedural Content Generation via Knowledge Transformation (PCG-KT)1 May 2023