Text-to-Video

Text-to-Video means: you write a sentence, and a computer program turns it into a video. One example would be “A dog runs in slow motion through shallow water, evening sun.” A few seconds or minutes later, a film clip is ready, usually between two and twenty seconds long. This video is not an assembly of existing footage. Every single frame is newly computed, pixel by pixel. Well-known programs of this kind are called Sora, Veo, or Runway.

What’s changing for film, advertising, and forgeries

Moving images used to be expensive. For a ten-second commercial you needed a camera, lighting, actors, and editing. Text-to-Video pushes these costs down to nearly zero for certain purposes. Advertising agencies use it for first drafts, so-called storyboards, meaning rough sketches of the later film. Instead of drawing, you type the sequence and see it in motion immediately.

For creative professions, this is a double-edged sword. Those who produce explainer videos or background footage face strong competition. Those who develop ideas gain a fast tool. In the film industry, there have already been strikes over this, in which actors and writers demanded rules for the use of AI.

The second consequence is more unpleasant. A video appears more credible to people than a text or a photo. If every person can invent convincing video scenes, the evidentiary value of footage declines. That’s why digital legislation in the EU obligates providers to label artificially generated content. Many models embed an invisible watermark in their videos, meaning a hidden marker within the image.

From noise to a moving scene

Almost all of these systems work on the principle of diffusion. The program starts with pure image noise, meaning a flicker like an old television with no reception. Then it removes this noise in many small steps. At each step, it estimates which image might be hiding behind the flicker. The typed text steers this estimate: it guides it toward dog, water, and evening sun.

The crucial difference from an image generator lies in time. A video is not a sequence of independent images. The dog must be the same dog in the next frame, and it must move in a physically plausible way. The model therefore computes with entire stacks of frames simultaneously and pays attention to the relationships between them. It learned this from millions of videos with accompanying descriptive texts.

A common misconception is that the model has understood physics. It has merely seen what water looks like in countless videos. That’s why these systems fail at tasks that require genuine understanding. A breaking glass sometimes reassembles itself, fingers multiply, and text in the image turns into a jumble of letters. Length also remains limited, because computational effort and errors grow sharply with every additional second.

Where you can already see such clips today

On TikTok, Instagram, and YouTube, large quantities of these videos are already circulating. Typical examples are short landscape shots, absurd animal scenes, or backgrounds for music tracks. Often you can recognize them by restless details at the edge of the frame or by an oddly smooth light. Some platforms display a notice labeled “AI-generated.”

In business news, Text-to-Video models appear as prestige projects. OpenAI, Google, and Chinese providers such as Kuaishou unveil new versions with great fanfare. For investors, the term is interesting because such models consume enormous computing power. Every second of video costs a multiple of a chatbot text, which drives demand for graphics chips and data centers.

You can try this out yourself. Several providers allow a limited number of free attempts per month, after which you pay for a subscription. One useful observation: the more precise the description, the more usable the result. Details about camera work, time of day, and visual style improve the video far more than a longer plot.