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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
In this paper we create 3 different sparse data sets from video game assets and train them with StyleGAN2 to generate new artwork based on the previously existing artworks of the video game in question
📌 3 Key Takeaways
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Objective & Hypothesis:GANs have been getting better and better each year.
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Key Findings & Data:The state of the art GAN models for generating 2D images have become so good it is hard to differentiate generated images nowadays.
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Conclusion & Impact:In this paper we create 3 different sparse data sets from video game assets and train them with StyleGAN2 to generate new artwork based on the previously existing artworks of the video game in question
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Generative art · Videogame · StyleGAN
Abstract
GANs have been getting better and better each year. The state of the art GAN models for generating 2D images have become so good it is hard to differentiate generated images nowadays. In this paper we create 3 different sparse data sets from video game assets and train them with StyleGAN2 to generate new artwork based on the previously existing artworks of the video game in question
Keywords
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Cite This Article
ERGEN, İ. (2023). Generating video game characters using StyleGAN2. Journal of Awareness, Cilt: 8 - Sayı: 1, 57–61. https://ajindex.com/articles/880733