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This paper presents a groundbreaking approach to real-time text-to-video synthesis, leveraging Large Language Models (LLMs) combined with generative video architectures. Through an examination of the intersection of computer vision and natural language processing, the dynamic conversion of textual descriptions is investigated into realistic, coherent video outputs in real-time. The study addresses key challenges, including multimodal fusion, temporal consistency, and fine-grained control over video generation. Additionally, novel real-time generation techniques are introduced that enable users to modify text inputs and receive instantaneous visual updates interactively. Experimental evaluations demonstrate state-of-the-art performance in text-guided video generation tasks, with applications spanning entertainment, advertising, education, and interactive storytelling. The proposed model maintains a low latency (35 ms) for real-time applications while outperforming current techniques in terms of temporal consistency and video quality (SSIM of 18.7, FID of 0.88). This research sets a new paradigm in AI-generated media, opening pathways for personalized, dynamic, and immersive video content creation.

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Real-Time Text-to-Video Synthesis Using Large Language Models

  • 会议论文

  • 发表日期：

    2026年

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  • Mitali Das,
  • Yatharth Srivastava,
  • Manish Yerram,
  • Mithun Shenoy,
  • Jyoti Parashar,
  • Virendra Singh Kushwah

摘要

This paper presents a groundbreaking approach to real-time text-to-video synthesis, leveraging Large Language Models (LLMs) combined with generative video architectures. Through an examination of the intersection of computer vision and natural language processing, the dynamic conversion of textual descriptions is investigated into realistic, coherent video outputs in real-time. The study addresses key challenges, including multimodal fusion, temporal consistency, and fine-grained control over video generation. Additionally, novel real-time generation techniques are introduced that enable users to modify text inputs and receive instantaneous visual updates interactively. Experimental evaluations demonstrate state-of-the-art performance in text-guided video generation tasks, with applications spanning entertainment, advertising, education, and interactive storytelling. The proposed model maintains a low latency (35 ms) for real-time applications while outperforming current techniques in terms of temporal consistency and video quality (SSIM of 18.7, FID of 0.88). This research sets a new paradigm in AI-generated media, opening pathways for personalized, dynamic, and immersive video content creation.