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Generating video game characters using StyleGAN2

Yıl 2023, Cilt: 8 Sayı: 1, 57 - 61, 03.02.2023

Öz

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

Kaynakça

  • BRADISKI, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000.
  • Karras et al. Analyzing and improving the image quality of StyleGAN.
  • Karras et al. A style-based generator architecture for generative adversarial networks.
  • Karras et al. Training generative adversarial networks with limited data.
  • The ImageMagick Development Team. Imagemagick.
Yıl 2023, Cilt: 8 Sayı: 1, 57 - 61, 03.02.2023

Öz

Kaynakça

  • BRADISKI, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000.
  • Karras et al. Analyzing and improving the image quality of StyleGAN.
  • Karras et al. A style-based generator architecture for generative adversarial networks.
  • Karras et al. Training generative adversarial networks with limited data.
  • The ImageMagick Development Team. Imagemagick.
Toplam 5 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

İsmail Ergen Bu kişi benim 0000-0002-8847-7262

Yayımlanma Tarihi 3 Şubat 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 8 Sayı: 1

Kaynak Göster

APA Ergen, İ. (2023). Generating video game characters using StyleGAN2. Journal of Awareness, 8(1), 57-61.