Text to Video Generation Using Natural Language Processing

Abstract

Text-to-video generation aims to automate the complex, labor-intensive process of creating compelling video content from textual input by leveraging advances in natural language processing (NLP) and artificial intelligence (AI). This paper presents a novel system that interprets user-provided text, extracts key themes, and synthesizes multimedia elements—including relevant images, video clips, voiceovers, and subtitles—into a cohesive video output with minimal manual intervention. The proposed platform features an intuitive web interface, deep learning models for semantic text analysis, and automated multimedia retrieval and assembly. By dramatically reducing time, technical barriers, and production costs, our system empowers educators, marketers, and content creators to produce tailored, high-quality videos rapidly and at scale. Experimental evaluation demonstrates efficient workflow, robust customization, and broad usability across multiple domains. This approach has the potential to democratize video creation, making it more accessible for diverse users and applications in the digital era.

Introduction

The text presents an AI-based text-to-video generation system designed to simplify and automate the creation of professional-quality videos. As video becomes a dominant medium for communication, education, and marketing, traditional video production remains time-consuming, expensive, and skill-intensive. Advances in Natural Language Processing (NLP) and Artificial Intelligence (AI) enable automated solutions that overcome these barriers by converting written text into cohesive videos with minimal human effort.

The proposed system analyzes user-provided text to extract key themes, retrieves relevant images and video clips, generates natural-sounding voiceovers using text-to-speech models, and automatically creates subtitles. These elements are assembled into a complete video using AI-driven workflows and video editing libraries. The platform is designed to be accessible, scalable, and user-friendly, making it suitable for educators, students, marketers, and small businesses.

A literature survey highlights key technologies supporting the system, including text-to-speech alignment, NLP-driven script understanding, multimodal video retrieval, generative AI architectures, and neural speech synthesis models such as Tacotron. These studies provide the foundation for synchronizing narration with visuals, semantic media selection, and efficient system design.

The research methodology follows a multi-stage pipeline involving text preprocessing with transformer models, multimedia retrieval from open repositories, automated voiceover and subtitle generation, and final video assembly. The system addresses limitations of existing tools, which often require subscriptions or manual intervention, by offering an end-to-end automated solution.

Results demonstrate that the platform successfully generates high-quality, shareable videos with strong alignment between visuals and narration, while providing real-time feedback and an intuitive interface. User testing indicates high satisfaction and ease of use. Overall, the project shows that AI-driven text-to-video generation can significantly reduce production effort, broaden access to multimedia creation, and support diverse applications in education, marketing, and digital content creation.

Conclusion

The AI Video Generator Hub project has successfully demonstrated the feasibility and effectiveness of automated text-to-video creation using state-of-the-art natural language processing and deep learning techniques. By offering an intuitive web interface and streamlined workflow, the system enables users to transform written scripts into fully narrated videos quickly and with minimal technical effort. Testing and user feedback confirmed high-quality results and substantial efficiency gains for educational, informational, and marketing application. This project represents a significant step toward democratizing video production, making it accessible to a broader range of users regardless of technical expertise. The flexible architecture and modular approach allow for continued evolution, with future directions including advanced personalization, support for multiple languages, and integration of generative animation. As AI video generators continue to advance, such systems are poised to transform digital content creation, enhance learning experiences, and drive innovation in visual storytelling.

References

[1] Ahn, Y., Chae, J., & Shin, J. W. (2025). Text-to-Speech Based on Speech-Assisted Text to-Video Alignment and Masked Unit Prediction. (As listed in the Literature Survey) [2] P., I. P., M., M., R., A., S., S. H., & R., H. (2024). Transforming Text to Video: Leveraging Advanced Generative AI Techniques. (As listed in the Literature Survey) [3] Bharathi, P. L., Sathvig, S., Siromita, A., & Pugalenthi, R. (2023). Text to Video Generation using Natural Language Processing. (As listed in the Literature Survey) [4] Dong, J., Wang, Y., Chen, X., Qu, X., Li, X., He, Y., & Wang, X. (2022). Reading Strategy Inspired Visual Representation Learning for Text-to-Video Retrieval. arXiv preprint arXiv:2201.09168. [5] M., M. R. K., Kuriakose, J., D S, K. P., & Murthy, H. A. (2021). Lip-syncing efforts for transcreating lecture videos in Indian languages. In Proc. 11th ISCA Speech Synthesis Workshop (pp. 216–221). [6] Lu, J., Sisman, B., Liu, R., Zhang, M., & Li, H. (2022). VisualTTS: TTS with accurate lip-speech synchronization for automatic voice over. In Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (pp. 8032–8036). [7] Lu, J., Sisman, B., Zhang, M., & Li, H. (2023). High-quality automatic voice over with accurate alignment: Supervision through self-supervised discrete speech units. arXiv preprint arXiv:2306.17005. [8] Wang, Y., Skerry-Ryan, R., Stanton, D., Wu, Y., Chen, Y., Battenberg, E., Clark, J., Prenger, R., & Isola, P. (2017). Tacotron: Towards end-to-end speech synthesis. In Proc. Interspeech. (A foundational paper for modern neural TTS systems used in voice-over work).

Copyright

Copyright © 2025 Vidhya K, Bharath K, Bharath Kumar, Chaithanya B R. 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.