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تسجيل مستخدم جديدGenerating Gameplay-Relevant Art Assets with Transfer Learning
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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 Pokemon sprites and Pokemon type information, since types are one of the games core mechanics and they directly impact the games visuals. Our experimental results indicate that adopting a transfer learning approach can help to improve visual quality and stability over unseen data.
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