This paper covers the development, testing, and implementation of an automatic framework for converting common images of pets into a Pokémon cartoon with the style of a Pokémon trading card. The technique will first implement object detection for common animals to facilitate image segmentation and apply the appropriate style transfer model to ensure the most aesthetic stylization. It explores various methods to address artifacts in the results of common neural style transfer techniques using Generative Adversarial Networks (GANs). This research sets up a framework to create an app that converts user-submitted pet pictures to Pokémon styled images using the most effective GAN framework.
Hedge, Michael B.; Nelson, Morgan; Pengilly, Thomas; and Weatherford, Michael
"PokéGAN: P2P (Pet to Pokémon) Stylizer,"
SMU Data Science Review: Vol. 5:
2, Article 10.
Available at: https://scholar.smu.edu/datasciencereview/vol5/iss2/10