Large Language Models (LLMs) are revolutionizing how we access information, generate content and develop ideas in the evolving field of Generative AI (GenAI). These robust models are bridging the gap between content writing and multimedia by merging natural language processing and cutting-edge video generation technologies. Turning plain text into engaging videos – what once seemed out of reach – is now at our fingertips. This turn isn’t just a flash in the pan – it’s here to stay, redefining how we consume and process information.

Imagine taking a plain article or report and turning it into a visually rich experience – complete with animations, voiceovers and graphics – with minimal effort. That’s essentially what LLM-driven video creation offers. It makes content transformation simple, fast and seamless by removing the legwork. Whether you’re looking to turn heads in marketing or cut through the noise in education, this blog dives into how video generation through GenAI is raising the bar.

The objective

The core idea behind this blog is plain yet impressive. We take any text document (in formats like .pdf, .docx or plain text) and turn it into a visually appealing video. This can have a wide range of applications – from turning document highlights into ultra-short videos and content previews, to generating quick short advertisements and bite-sized market teasers.

We’re combining the natural language capabilities of Mistral, an open-source LLM, with the generative visual power of CogVideoX, an open-source text-to-video model.

Step 1: Extracting text from the document

The conversion begins with a text document – it could be any long-form content. In this step, we focus on extracting the relevant text from the input document. This includes isolating meaningful content while removing unnecessary formatting. This ensures that the text that is passed to the LLM for summarization is clean and ready for efficient processing.

Step 2: Summarizing the content using Mistral

The second step is to convert the extracted text into a summary and that’s where Mistral, a robust open-source large language model, comes into the picture. There are many open-source LLMs suitable for text summarization, such as LLaMA, Phi, MPT, Falcon, etc. However, for this project, we’re specifically using Mistral for its performance and efficiency.

Using Mistral, we convert the extracted text into a precise and coherent summary. This is important because video generation models need focused and sharp prompts and feeding them raw, lengthy text can lead to cluttered or confusing results. Mistral’s summarization capabilities help redefine long content into crisp, meaningful summaries.

Step 3: Creating a story from the summary:

Once summarization is done, we reframe it as a story – again using Mistral LLM. This is an innovative step that involves modifying the informative tone in the summary into more narrative-driven content. We incorporate characters, scenarios or flows that enhance both the visual and emotional aspects of the video.

For example, if the input document is about AI in daily life, the summary might outline the associated benefits and risks. We could build a short story around people using smart devices – highlighting how AI shapes their everyday decisions.

This conversion from abstract to personal is essential because videos create a greater impact when built around relatable stories rather than just abstract information.

Step 4: Generating the video using CogVideoX

Now that we have the story, the final step is to pass the textual prompts to CogVideoX, an open-source model for text-to-video generation. CogVideoX is a diffusion transformer that interprets descriptive text prompts and converts them into short video clips of 10 seconds with impressive visuals.

We provide CogVideoX with a story, specifying the details required to improve the quality of the output video. The outcome? A fully AI-generated video that indicates the original content but in a way that is easy to understand, visually appealing and highly accessible.

Why does it make a difference?

1. Complete automation: Unlike most tools that focus only on one part of the process, this project covers the whole journey – extracting text from .pdf, .docx and .txt documents, summarizing with LLMs and ultimately converting into videos.
2. Unique workflow: It’s rare to come across an open-source tool that combines document processing, text summarization and video generation in one seamless flow – making this approach innovative.
3. No creative bottlenecks: Standard video creation demands time-consuming scripting and editing. This system eliminates that by using AI to generate both script and visuals – saving hours of manual work.
4. Open-source and lightweight: Using open-source models like Mistral and CogVideoX, it’s flexible, cost-effective and accessible to educators, developers and startups. Made for modern media: Ideal for creating 10-second video teasers, summaries and promos – effortlessly transforming plain text into engaging, impactful short-form videos.

What’s next?

1. Adding voiceovers: Introduce AI-generated voiceovers by leveraging text-to-speech LLMs that sound natural and adapt to tone for richer storytelling.
2. Talking avatars: Visual characters that can deliver summaries by syncing speech with expressions, making the videos visually appealing.
3. Multilingual support: Enable multiple languages for both voice and text to make content inclusive and globally relatable.
4. Tone adaptation: Automatically tune the voiceover based on the intent – whether it’s professional, persuasive or emotive storytelling.
5. Dynamic UI: Integrate visual triggers like clickable buttons, pop-up highlights and guided checkpoints throughout the timeline to make videos more interactive.