Cycle Generative Adversarial Network for Unpaired Sketch-to-Character Translation

  • Leena AlsaatiEmail author
  • Siti Zaiton Mohd HashimEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


This research investigated the capabilities of Cycle-Consistent adversarial network (Cycle GAN) in the application of stick figure sketches to cartoon character translation. Few studies focused on the task of generating a variety of poses and facial expression of cartoon characters from simple sketches of stick figures, based on unpaired dataset samples. Furthermore, existing studies showed low performance in detecting rare pose features. In this research, two datasets have been created which consists of paired and unpaired images of manually drawn sketches and cartoon characters. The performance of Cycle GAN has been compared against a paired based model, Pix2Pix, by using qualitative and quantitative evaluation measurements. Results show that Pix2Pix outperforms Cycle GAN in accurately mapping cartoon characters to stick figures. Despite that, the Cycle GAN still managed to produce competing results.


Cycle-consistent GAN Pix2pix Image translation 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universiti Teknologi MalaysiaSkudaiMalaysia

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