Key Learning Points:

  • Video generation technology refers to a system where AI creates videos from short text descriptions, and it has the potential to impact both our daily lives and work.
  • This technology uses methods such as “diffusion models” and “GANs,” and because videos contain more information than images, they are considered more complex to generate.
  • While its use is expanding in areas like advertising and film production, there are still challenges such as unnatural visuals and legal concerns.

A New World of Visual Expression Created by AI

Have you ever thought, “I wish there were a video like this”? For example, imagine turning the story or image in your mind into an actual video. That kind of dream is slowly becoming a reality today.

The technology gaining attention for this is called “video generation.” It may still be an unfamiliar term, but it’s something that could gradually become part of our everyday lives and work.

How AI Creates Videos from Short Text

In simple terms, video generation is a technology where AI creates videos. To explain a bit more, when a person gives a short text—like “a dog walking along the beach at sunset” (this kind of input is called a “prompt”)—the AI uses that description to generate the scene as a video from scratch.

This belongs to the broader field of “generative AI,” which includes technologies that automatically create images or text. But unlike still images, videos need to express motion and the passage of time. This means they involve much more information and require more advanced processing.

To make this possible, techniques like “diffusion models” and “GANs (Generative Adversarial Networks)” are used. These methods allow the AI to go through repeated trial-and-error processes to get closer to natural-looking results. In particular, GANs involve two AIs competing with each other—one generating fake data and the other trying to detect it—which helps improve quality over time. Training these systems requires large amounts of video data.

Where Is It Being Used? And What Challenges Remain?

So where is this technology being put to use?

In advertising, for instance, it’s gaining attention as a way to quickly create impactful promotional videos. In film and animation production, it’s starting to be used during early concept stages—before full-scale filming or drawing begins—to help visualize ideas.

More recently, services have appeared that let users input simple text prompts and receive anime-style videos based on their own stories. What once required specialized knowledge or equipment is now becoming much more accessible.

However, there are still challenges. Sometimes AI-generated videos contain awkward movements or mismatches between elements—things humans can spot right away as feeling off. There are also unresolved legal issues around copyright and image rights. For example, there’s a risk that someone who looks very similar to a real person might appear unintentionally in an AI-generated video. So safety and ethical considerations remain important.

A Future of Expression Evolving with Imagination

Even so, this technology holds great promise. Tasks that once took large teams many hours can now be tested in just moments. More importantly, it’s evolving into a tool that supports people’s desire to express themselves—that’s what makes it so fascinating.

Of course, it’s still developing. But even now we stand at the doorway of new ways to express ourselves through video. The desire to say “I want to see something like this” or “I want to create this world” could become the driving force behind further progress in this field.

In our next article, we’ll take a closer look at diffusion models—a key part of how video generation works. Let’s continue building our understanding together one step at a time.

Glossary

Video Generation: A technology where AI creates videos based on given instructions. For example, from the sentence “a dog walking along the beach at sunset,” it generates that scene as moving visuals.

Prompt: A short sentence or phrase used to tell the AI what kind of content to create. In video generation, the prompt largely determines what kind of video will be produced.

GAN (Generative Adversarial Network): A system where two AIs compete with each other during training—one generates fake data while the other tries to detect it—resulting in more realistic images or videos over time.