Animating Text Descriptions into Characters: A Comparative Review of Generative Models
Zarif, S., Amin, K., Najjar, A., Wagdy, M. (2025). Animating Text Descriptions into Characters: A Comparative Review of Generative Models. EKB Journal Management System, 12(1), 43-66. doi: 10.21608/ijci.2024.307030.1167
Sameh Zarif; Khalid M. Amin; Abdalfatah Najjar; Marian Wagdy. "Animating Text Descriptions into Characters: A Comparative Review of Generative Models". EKB Journal Management System, 12, 1, 2025, 43-66. doi: 10.21608/ijci.2024.307030.1167
Zarif, S., Amin, K., Najjar, A., Wagdy, M. (2025). 'Animating Text Descriptions into Characters: A Comparative Review of Generative Models', EKB Journal Management System, 12(1), pp. 43-66. doi: 10.21608/ijci.2024.307030.1167
Zarif, S., Amin, K., Najjar, A., Wagdy, M. Animating Text Descriptions into Characters: A Comparative Review of Generative Models. EKB Journal Management System, 2025; 12(1): 43-66. doi: 10.21608/ijci.2024.307030.1167

Animating Text Descriptions into Characters: A Comparative Review of Generative Models

IJCI. International Journal of Computers and Information
Article 4, Volume 12, Issue 1, January 2025, Page 43-66  XML PDF (2.12 MB)
Document Type: Original Article
DOI: 10.21608/ijci.2024.307030.1167
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Authors
Sameh Zarif email 1; Khalid M. Aminorcid 2; Abdalfatah Najjar3; Marian Wagdy4
1Faculty of computers and information, Menofia university
2Information technology dept., Faculty of computers and information, Menofia university
3Information Technology Department
4Department of Information Technology, Faculty of Computers and Information
Abstract
In recent times, the advent of text-to-image generative AI technologies, commonly referred to as AI Image Generators, has captured widespread attention for their remarkable capability to swiftly produce visuals based on textual descriptions. This development has ignited diverse perspectives, especially within the animation sector, making it a focal point for scholarly investigation due to the introduction of generative adversarial networks. Despite the progress, the domain confronts several challenges that necessitate further scholarly inquiry, including the production of high-resolution imagery featuring multiple elements and the creation of evaluation metrics that align with human assessment. Moreover, the outcomes of this study reveal that AI Image Generators hold the potential to substantially boost creative outputs in animation by providing a variety of characters and settings with superior visual quality. This review aims to examine and compare the extensive body of work in this field comprehensively. It will detail the algorithms employed, identify existing issues, and propose new directions for research.
Keywords
generative-art; AnimeGAN; text-to-image; animation-character; generative-adversarial-network
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