Version 1
: Received: 2 September 2024 / Approved: 3 September 2024 / Online: 3 September 2024 (07:35:22 CEST)
How to cite:
Nyame, L.; Bengesi, S. Generative Artificial Intelligence Trend on Video Generation. Preprints2024, 2024090195. https://doi.org/10.20944/preprints202409.0195.v1
Nyame, L.; Bengesi, S. Generative Artificial Intelligence Trend on Video Generation. Preprints 2024, 2024090195. https://doi.org/10.20944/preprints202409.0195.v1
Nyame, L.; Bengesi, S. Generative Artificial Intelligence Trend on Video Generation. Preprints2024, 2024090195. https://doi.org/10.20944/preprints202409.0195.v1
APA Style
Nyame, L., & Bengesi, S. (2024). Generative Artificial Intelligence Trend on Video Generation. Preprints. https://doi.org/10.20944/preprints202409.0195.v1
Chicago/Turabian Style
Nyame, L. and Staphord Bengesi. 2024 "Generative Artificial Intelligence Trend on Video Generation" Preprints. https://doi.org/10.20944/preprints202409.0195.v1
Abstract
This study comprehensively reviews Generative AI and its relationship to video generation, emphasizing video cloning. The study is intended for Geekscode LLC’s strategic development; Geekscode LLC is a new company considering creating video cloning software as its main product. Furthermore, the study seeks to enhance the understanding of researchers in the information technology field and provide information technology professionals with insights into video generation using modern AI tools. This study highlights both the transformative potential and the legal, ethical, and technical challenges of AI-driven video creation. The study examines these key aspects of Video generation, including diffusion models, autoregressive models, input modalities, and Generative Adversarial Networks. This study highlights the significance of interdisciplinary approaches in navigating the area of AI-powered video production and the need to balance innovation and responsible ethical practices.
Keywords
Artificial Intelligence; Generative Artificial Intelligence; Video Cloning; Variation Autoencoders
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
