# Generating Gameplay-Relevant Art Assets with Transfer Learning

arXiv: https://arxiv.org/abs/2010.01681 · HTML: https://mlsanity.com/paper/2010.01681/

## Key facts

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

## Built on by (most cited first, 4 of 4)

- [SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches](https://mlsanity.com/paper/2209.00185/) (2022)
- [Pixel VQ-VAEs for Improved Pixel Art Representation](https://mlsanity.com/paper/2203.12130/) (2022)
- [Generating Pixel Art Character Sprites using GANs](https://mlsanity.com/paper/2208.06413/) (2022)
- [A Missing Data Imputation GAN for Character Sprite Generation](https://mlsanity.com/paper/2409.10721/) (2024)

## Abstract (arXiv)

In game development, designing compelling visual assets that convey gameplay-relevant features requires time and experience. Recent image generation methods that create high-quality content could reduce development costs, but these approaches do not consider game mechanics. We propose a Convolutional Variational Autoencoder (CVAE) system to modify and generate new game visuals based on their gameplay relevance. We test this approach with Pok'emon sprites and Pok'emon type information, since types are one of the game's core mechanics and they directly impact the game's visuals. Our experimental results indicate that adopting a transfer learning approach can help to improve visual quality and stability over unseen data.