Abstract
The growing demand for multimedia content has spurred the need to automate the conversion of textual information into video formats. This paper proposes a novel approach for converting Press Information Bureau (PIB) press releases into videos using Generative Adversarial Networks (GANs). By leveraging GANs, a state-of-the-art deep learning model, we aim to generate video content from textual data, facilitating the dynamic presentation of information from government press releases. This process could significantly enhance the accessibility and engagement of press releases, making them more suitable for modern multimedia platforms. The paper discusses the methodology, implementation, and evaluation of this system, providing a foundation for further research in automatic text-to-video conversion.