  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.
