Generating Gameplay Relevant Art Assets with Transfer Learning

In game development, designing compelling visual assets that convey gameplay-relevant features requires time and experience . We propose a Convolutional Variational Autoencoder (CVAE) system to modify and generate new game visuals based on their gameplay relevance . Our experimental results indicate that adopting a transfer learning approach can help to improve visual quality and stability over unseen data . The CVAE system is based on Pok\’emon sprites and type information, since types are one of the game’s core mechanics and they directly impact the visuals .

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Keywords : gameplay - game - based - visuals - system -

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