Journal of Graphic Engineering and Design

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GANS for realistic animation: A qualitative study using ATLAS.ti

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  • Mars Caroline Wibowo,
  • Aris Sarwo Nugroho,
  • Edy Jogatama Purhita
Mars Caroline Wibowo
Universitas Sains dan Teknologi Komputer, Salatiga, Indonesia
Aris Sarwo Nugroho
Universitas Sains dan Teknologi Komputer, Salatiga, Indonesia
Edy Jogatama Purhita
Universitas Sains dan Teknologi Komputer, Salatiga, Indonesia

Published 2026-05-05

abstract views: 0 // Full text article (PDF): 0


Keywords

  • generative adversarial networks (GANs),
  • realistic animation,
  • motion quality analysis,
  • texture and visual composition

How to Cite

Caroline Wibowo, M., Sarwo Nugroho, A., & Jogatama Purhita, E. (2026). GANS for realistic animation: A qualitative study using ATLAS.ti. Journal of Graphic Engineering and Design. Retrieved from https://sp.ftn.uns.ac.rs/index.php/jged/article/view/2457

Copyright (c) 2026 © 2026 Authors. Published by the University of Novi Sad, Faculty of Technical Sciences, Department of Graphic Engineering and Design. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license 4.0 Serbia

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This study aims to explore the effectiveness of GANs in generating animations that achieve high levels of realism, focusing on motion quality, texture detail, visual composition, and frame-to-frame coherence. A qualitative approach was employed using ATLAS.ti to analyze outputs from models such as MoCo-GAN and StyleGAN3. The dataset included 110 animations, from which key visual elements were coded and analyzed thematically. The findings reveal that 68% of the animations demonstrated smooth motion transitions, while 20% exhibited jerky movements and 12% contained motion artifacts. Similarly, 70% of the animations featured highly detailed textures, but 20% had flat backgrounds, and 10% showed lighting inconsistencies. Visual compositions with strategic framing and depth perception were observed in 55% and 30% of the animations, respectively, whereas only 15% maintained symmetrical layouts. These results underscore the strengths and limitations of GANs in achieving realism, particularly in complex scenarios. This study contributes to the growing body of literature on GAN applications in animation by identifying critical visual factors that enhance aesthetic and narrative coherence. Practical implications include guiding designers and developers in leveraging GANs for high-quality animation production. Future research is recommended to address existing technical challenges and evaluate audience responses to GAN-generated animations, paving the way for more dynamic and engaging visual content.

Article history: Received (December 11, 2024); Revised (February 9, 2025); Accepted (October 15, 2025)

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