@article{4175,
  author = {P. Logeswari, N.R Solomon Jebaraj, Banu Priya G},
  title = {Comparative Analysis of AI Tools for Video Production},
  journal = {Journal of Information Technology Review},
  year = {2024},
  volume = {15},
  number = {4},
  doi = {https://doi.org/10.6025/jitr/2024/15/4/132-137 },
  url = {https://www.dline.info/jitr/fulltext/v15n4/jitrv15n4_4.pdf},
  abstract = {Video creation is one of the many artistic fields significantly impacted by the advancements in artificial intelligence (AI). This study paper examines ten prominent AI video generation tools: Runway, Pictory, Deepbrain AI, Synthesia, Colossyan, Hour One, D-ID, Elai.io, HeyGen, and InVideo. We compare these tools based on their architecture, learning strategies, algorithms (where applicable), benefits, and drawbacks. Our analysis reveals that most of these tools employ unsupervised learning techniques, likely using generative models such as Generative Adversarial Networks (GANs) for video production. Additionally, many tools feature text-to-speech conversion and offer functionalities like script-based video creation, customisable avatars, and AI-driven editing capabilities. This study highlights the potential of AI video generation tools to democratise video creation, making it accessible to those without editing experience. However, it also addresses some drawbacks, including limited customisation options, misuse (e.g., deepfakes) risk, and varying video quality among different tools. The conclusion emphasises the need for further advancements in AI video technology, focusing on enhancing user control, ensuring ethical use, and consistently improving output quality. This research paves the way for a future where AI facilitates accessible and efficient video production for various applications.},
}