How does AI generate videos?

Last updated: May 2026

You type "a golden retriever puppy chasing bubbles in a sunlit garden," wait a moment, and get a video that looks like it was filmed on a real camera. No camera existed. No puppy existed. So where did the video come from? The answer involves a surprisingly counterintuitive trick.

A few years ago, the idea of typing a sentence and getting a realistic video back would have sounded absurd. Now it's a product you can use in a browser. The technology behind it is clever, and the central idea is so odd that it's worth stating up front: AI makes video by starting with pure visual noise, like TV static, and then carefully removing the noise until a picture appears.

That sounds backwards. You don't build a sculpture by starting with random rubble and removing the bits that aren't the statue. Except that's more or less exactly what these systems do. Let's unpack how, and why it works.

Start with the still image version

Video is hard, so start with its simpler cousin: AI image generation. The same core method powers both, and it's easier to picture with a single image.

The technique is called diffusion. Imagine taking a clear photograph and adding a tiny bit of random speckled noise to it. Then a bit more. And more, over many steps, until the original picture is completely buried under static and you can't tell what it was. That's an easy process to do. Anyone can add noise to a picture.

The trick is to teach an AI to run that process in reverse. During training, the system is shown millions of images at every stage of being noised up, and it learns to predict what the slightly-less-noisy version looked like. Do that well enough, and you get a model that can take a screen of pure static and, step by step, remove noise until a clean, detailed image emerges that was never photographed at all.¹

OpenAI described its own video model in exactly these terms: it generates a video by starting with something that looks like static noise and gradually transforms it by removing the noise over many steps.²

Where the text prompt comes in

So far this would just produce random pictures. What makes it produce the specific thing you asked for?

The noise-removal process is guided by your prompt. As the model cleans up the static, it's continually steered toward an image that matches the meaning of your words. Ask for a "sunlit garden" and the denoising is nudged, at every step, toward greens and warm light and garden shapes rather than, say, a snowy street. The text acts like a set of instructions whispering "make it look more like this" throughout the whole cleanup.³

To do this, the system first has to understand your prompt. It breaks the sentence down into its meaning, identifying the subject, the action, the setting, and the mood, then uses that understanding to shape the denoising.³

Now add the hard part: time

A video is not one image. It's many images per second, and they have to relate to each other. A puppy in one frame must be the same puppy, in a slightly different position, in the next. Get this wrong and you get the melting, morphing weirdness that early AI video was infamous for.

This challenge has a name: temporal consistency, meaning consistency across time.¹ Solving it is what separates convincing AI video from a nightmarish slideshow.

The breakthrough was to stop treating a video as a stack of separate images and start treating it as a single block of information with width, height, and time baked in together. Modern systems chop a video into small three-dimensional chunks, sometimes called spacetime patches, that each cover a little area of the picture across a little slice of time.⁴ The model handles these chunks the way a chatbot handles words, learning how they fit together across the whole clip. Because it considers space and time at once rather than separately, motion comes out smooth and objects stay stable.⁵

The engine behind the modern versions

The most capable video generators combine two ideas you may have heard of. The diffusion process does the denoising, and a transformer, the same type of architecture behind chatbots like ChatGPT, keeps track of how everything in the scene relates across the whole video.⁵ This combination is sometimes labelled a "diffusion transformer." You don't need the label. The point is that one part removes noise to build the picture, and the other part maintains the big-picture consistency so the result holds together as a coherent scene.

This approach powers the current generation of tools from a number of companies. Alongside OpenAI's work, there are systems like Runway's Gen-3, Google's efforts, Luma's Dream Machine, Kling, and Meta's Movie Gen, several of which can now generate clips with synchronised sound as well as picture.⁶

Why AI video still gets things subtly wrong

Even the best current systems have tells, and understanding the method explains them. The model has learned what things tend to look like, but it has no built-in understanding of physics or how the world actually works. So it can produce a person with a convincing face but hands that briefly have six fingers, or water that flows in a way no real water would, or an object that quietly changes shape when nobody's watching it in the frame.⁷

These errors happen because the system is generating what's visually plausible, not simulating a real scene. It's painting frames that look right, not running a physics engine. When plausibility and reality drift apart, you get the small glitches that give AI video away, at least for now.

The short version

To make a video, an AI starts with a field of random noise and removes it step by step, guided at every step by the meaning of your prompt, until a clear picture emerges. To turn that into moving video rather than a single image, it treats the whole clip as one connected block of space and time, working in small three-dimensional chunks so that motion stays smooth and objects stay consistent from frame to frame. A diffusion process builds the images and a transformer keeps the whole scene coherent.

It's a strange way to make a picture, sculpting signal out of static, but it turns out to be one of the most powerful ideas in modern AI. And once you know it's happening, the occasional flicker of a sixth finger makes a lot more sense.

Sources

1. "AI Video Generation in 2025: Sora's Limits, Runway's Breakthroughs," Oragen (2025) — oragenai.com
2. OpenAI, quoted in "What is Sora?" Latenode (2026) — latenode.com
3. "Complete Sora AI Video Generation Guide," SoraAINow (2026) — soraainow.com
4. "What Is OpenAI Sora 2?" MindStudio (2026) — mindstudio.ai
5. "Video Diffusion Models: A Survey," arXiv (2024) — arxiv.org
6. Ibid. (Runway Gen-3, Luma, Kling, Meta Movie Gen comparison).
7. "What is Sora? OpenAI video generation model" (on physics limitations), Latenode (2026) — latenode.com