What They Don’t Tell You About Text-to-Video AI

When you’re typing a few words and watching them transform into a stunning video within seconds. Sounds like science fiction? Well, it’s not. That’s Text-to-Video, it’s likely to revolutionize how people make content in the future. In this blog, let’s talk about what they don’t tell you about text-to-video AI. 

What’s behind the magic?

Speaking of the magic of Text-to-Video, we have to talk about two important technologies: diffusion and transformers. These two form the main components of the system that converts your text into attractive videos.

Transformers

Transformers is revolutionary in generative AI boom, but  in text-to-video models primarily serve to transform the input text into a detailed, usable form to guide the diffusion process. This helps the model to associate the meaning and organization of the text with the video frames created. 

Transformers in LLMs like GPT-4 help understand how words are related to one another and thus create clear text. In general, the transformer design within LLMs aims to improve language modeling, which is helpful for the model to generate natural-sounding text.

Diffusion models

The diffusion part is responsible for the actual pixel-level output using the text encoding as the guiding signal. 

Diffusion models are a powerful type of generative model. They excel at image generation and solving inverse problems like denoising. These models refine a noise signal step by step. They start with what looks like random pixels. Gradually, the pixels form data that resembles real images or videos. It’s like carving a sculpture from a block of marble. The final shape emerges as the model makes many small changes.

The integration of transformers with diffusion models becomes a powerhouse mechanism in transforming text into video. The transformer converts the meaning of the text into a digital representation. The diffusion model turns this representation into video, starting from random noise. This approach enables the creation of marketing videos, educational resources, or short films. It is useful in many different situations.

Top Text-to-video AI models 

With this magic in text-to-video, let’s explore some key AI models. These models implement the latest technologies in the field.

Sora by OpenAI

Notice the texture and realism in the video. Sora by OpenAI is a leading text-to-video model. Sora uses advanced diffusion models to generate high-quality, realistic videos from text. It also relies on powerful transformer-based language understanding. This combination sets a new standard for text-to-video technology.

Some examples on their website are quite impressive. Sam Altman even took prompts from the comments and using Sora to produce high quality videos on the fly : 

VIDU

Well, if you think what you just saw is impressive, you may want to take a look at VIDU. Vidu is a Chinese text-to-video AI model from Tsinghua University and the Chinese AI startup ShengShu Technology. Many videos had gone viral because this AI can ‘revive’ old, vintage pictures and even make you dance with your doppelgangers. I believe your instagram feed had filled with video like this. VIDU is optimized for speed, it can churn out videos at alarmingly high speeds without sacrificing too much quality. VIDU is a great tool for any content developer who needs to churn out multiple videos in no time, with little loss in quality.

Here’s the kicker! How does Sora compare with VIDU? I’ve scrambled hundreds of examples off the internet, I personally feel Sora goes in-depth and intricate. It gives details that are of high quality, however, VIDU performs quite well in speed and efficiency, and easy to use. 

Veo in Vids by Google Deepmind

Veo is advertised for generating AI videos for custom footage in minutes, especially in long contexts. This makes it great for projects that require a smooth and fluent storytelling experience. It is complex, human-like but very complex motions.

As major studio like Pixar starts to embrace AI video technology,  it may be used for realistic animations or virtual avatar creation. That is very useful in applications that rely heavily on subtlety within human motion, such as in video gaming and virtual reality. 

Lumiere by Google Deep Mind

Lumiere, a space-time diffusion model for video generation by Google Deep Mind. It synthesizes videos with real, diverse, and coherent motion. Lumiere even allows users to create videos based on one still image combined with a text prompt, thus giving a truly novel approach to video creation. Such ability is extremely useful when having an initial image that is advantageous to any process,  for instance, I can see it largely used in marketing or social media posts.

Emu Video by Meta

Emu Video demonstrates Metas’ significant advancements in video creation. It presents a novel method that uses a text description to create an image that is then animated into a video. More accuracy in the finished product is made possible by this technique, which is advantageous for projects that call for particular visual components. I personally feel there’s still room for improvement after looking through their website and giving a few prompts a try. But Meta deserves credit for its groundbreaking work and candor in this area. 

Runway 

Runway is always a shining member in the text-to-video community and now they’re already in Gen 4 and it’s getting better and better through generations. 

With its ability to create videos from a variety of inputs, including text, image,s and short videos. Nowadays Runaway can produce cinematic high-resolution videos that surpass the capabilities of its predecessor, is completely changing the video generation landscape. 

I can see some practical usecase specifically for runaway, because it is an excellent choice for virtual reality gaming advertising, and short film production given it produces videos with remarkable temporal consistency, detailed visual details, and improved user control options. Not only does the Gen-4 model render video content more quickly and realistically than the Gen-2 and Gen 3 model but it also has advanced features like a motion brush and sophisticated camera adjustments. These developments allow artists to produce visual narratives that are even more complex and dynamic. 

All those AI models constantly push the boundaries of what’s possible, as the Text-to-Video AI landscape continues to evolve. You’ll learn their strengths and be better prepared to select the correct tool for you.

How multimodal AI got into the mix

Now that you find text-to-video or image-to-video interesting, it is fascinating to learn about multimodal AI. 

When say a AI systems that can understand and work with more than one type of data or modality like text, images, audio, or video, it is multimodal AI. 

This is an advanced AI models integrate various input formats to generate contextualized and versatile outputs, in contrast to traditional AI systems that are limited to processing a single kind of data. 

For instance, the AI built into the Ray-Ban Meta smart glasses can understand a spoken command in addition to an image process these inputs using a trained model and generate a response that could include text, audio or more image analysis. 

Multimodal models (like some transformers) can process and link information from text, images, and even sounds, creating a much richer understanding. This enables the AI to turn your written ideas into moving visuals that match the prompt.

Multimodal AI allows text-to-video models to “mix” language understanding and visual generation, making it possible to create videos that accurately reflect what you describe in text. It’s the backbone of how text prompts are translated into believable video content or even real movies someday!

Looking forward 

We’d love to hear your thoughts on this. Is there anything else interesting about text-to-video that we might have left out? Comment down below with your thoughts. Please feel free to follow me here on Medium and subscribe to my newsletter or my YouTube channel if you’d like to learn more, and share your thoughts in the comment section. I had to take some time off due to personal circumstances, but I will continue to practice my entrepreneurship muscle every week here in 2025! Stay tuned, and see you in the next one!