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

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Emerging Trends in Intelligent Computing and Informatics (IRICT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1073))

Abstract

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.

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Author information

Authors and Affiliations

  1. Universiti Teknologi Malaysia, Skudai, Malaysia

    Leena Alsaati & Siti Zaiton Mohd Hashim

Authors
  1. Leena Alsaati
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  2. Siti Zaiton Mohd Hashim
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Corresponding authors

Correspondence to Leena Alsaati or Siti Zaiton Mohd Hashim .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah Darul Aman, Malaysia

    Fathey Mohammed

  3. Management of Information Systems Department College of Business Administration, Taibah University, Yanbu, Saudi Arabia

    Nadhmi Gazem

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Alsaati, L., Hashim, S.Z.M. (2020). Cycle Generative Adversarial Network for Unpaired Sketch-to-Character Translation. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_31

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