English

tile2tile: Learning Game Filters for Platformer Style Transfer

Machine Learning 2022-08-17 v1

Abstract

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.

Cite

@article{arxiv.2208.07699,
  title  = {tile2tile: Learning Game Filters for Platformer Style Transfer},
  author = {Anurag Sarkar and Seth Cooper},
  journal= {arXiv preprint arXiv:2208.07699},
  year   = {2022}
}

Comments

Accepted to AIIDE 2022

R2 v1 2026-06-25T01:44:19.840Z