AI-Driven WebTV: An End-to-End Architecture for Automated Video Content Creation and Broadcasting

This paper presents developing and implementing an AI-driven WebTV system designed to automate the video content creation pipeline, from conceptualization to broadcasting. Leveraging open-source models such as Zeroscope for text-to-video generation and MusicGen for music synthesis, the system integrates large language models (LLMs) to create a seamless, modular production architecture. The research explores the challenges of combining different AI components into a cohesive framework, highlighting issues such as real-time processing, video quality optimization, and model interoperability. The findings demonstrate the system’s ability to generate coherent, good-quality video and audio content autonomously, significantly reducing the need for human intervention in traditional video production workflows. Although the AI-driven WebTV system illustrates the potential for scalable, automated media production, limitations in scene complexity, frame interpolation, and content consistency are identified. Future work is suggested to enhance the system’s scalability, real-time adaptability, and ethical content generation. This research underscores the transformative potential of AI in media production, offering a foundation for future exploration into fully autonomous digital content creation.