Visual Storytelling: Text-to-Video Conversion from Bibliometric Perspectives

Authors

  • Gaganpreet Kaur, Amandeep Kaur, Meenu Khurana

Keywords:

Text2Video, Education, Research, Bibliometric, Precision, adaptive storytelling.

Abstract

Building upon previous research on Text2Image, Text2Presentation, and Text2Video, this study adds value by critically evaluating a wide range of current methodologies, including different generative models, critical analysis of various approaches, highlighting the shortcomings, and suggesting and directing future research. Text-to-video generation is a cutting-edge and quickly developing field of artificial intelligence that uses computer vision and natural language processing to generate video material from textual descriptions.A popular and rigorous method for searching and assessing large volumes of scientific material is bibliometric analysis. As a result, we aim to provide a comprehensive overview of the bibliometric approach, emphasizing its various methodologies, along with detailed instructions that can be trusted to ensure that bibliometric analysis is carried out rigorously and confidently. To this end, we elucidate when and where to apply bibliometric analysis in relation to related techniques such as systematic literature reviews and meta-analyses. This work presents the first bibliometric analysis of text-to-video production, filling a significant gap in the literature by providing a comprehensive overview of major themes, prominent researchers, and developing areas within this rapidly evolving field. Taken as a whole, this research should be a useful tool for comprehending the many methods and approaches that can be used to conduct bibliometric analysis research.

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Published

12.06.2024

How to Cite

Gaganpreet Kaur. (2024). Visual Storytelling: Text-to-Video Conversion from Bibliometric Perspectives. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 3198–3207. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6814

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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