Why your AI videos look distorted—and 5 fixes that actually work

AI tools like Sora from OpenAI or Veo promise cinematic-quality videos at the touch of a button. That said, the results can sometimes look artificial or distorted. This usually isn’t a limitation of the model itself, it’s about how it’s used. In this guide, we’ll share five proven techniques to dramatically improve the quality of your AI-generated videos.

1. Describe the subject as specifically as possible

AI video models will usually fill in the gaps themselves, but that’s exactly the problem. That’s why you need to be crystal clear in your description. If you’re not specific, this will lead to to incorrect backgrounds, distorted objects, or unwanted details. Instead of a general description like “Create a 10 second clip of a cat playing,” you should be more detailed with the following:

• Appearance of the subject
• Environment and lighting
• Action and mood

In sticking with the cat example, you could write:

“A small, short-haired brown domestic cat with white paws plays with a stuffed animal in the shape of a squirrel. The scene takes place in a bright living room of a detached house, with warm daylight coming in through a window on the left. The floor is made of light wood, and a sofa can be seen blurred in the background. The cat nudges the toy with its paw, jumps back briefly, and then watches it curiously. The mood is calm, playful, and natural, the camera remains at the cat’s eye level and does not move.”

2. Use multiple runs

AI videos are not deterministic. This means that even with identical prompts, the results usually differ significantly. A failed video doesn’t automatically mean that the prompt was bad.

Experienced users deliberately create multiple versions of the same clip. Even small variations in movement, perspective, or timing can make the difference between unusable and surprisingly good.

The rule of thumb is simple: if five to ten runs don’t produce a convincing result, the problem doesn’t lie with the tool, it’s the prompt.

3. Keep scenes deliberately short and focused

Most AI video generators are designed to produce short, self-contained sequences lasting only a few seconds. If several actions, locations, or perspective changes are combined within a single clip, the likelihood of errors increases significantly: characters suddenly change their appearance, objects disappear, and movements often appear unnatural or jerky.

Prompts that describe a complete sequence are particularly problematic. Here’s an example:

“A person leaves their flat in the morning, walks through a busy street, enters a café, orders a coffee, sits down by the window, and looks out thoughtfully.”

Many AI models are still very unreliable when it comes to depicting such dramatic arcs. In the generated video below, numerous errors and inconsistencies appear right from the start, as the sequences appear out of order:

Sora/PC-Welt

A better description would be:

“A person is sitting in a small café at a window seat. Warm light falls in from the right. The person is drinking coffee and looking calmly out the window. The camera is static, slightly to the side at face level. The mood is calm and thoughtful.”

The video generated from this prompt is not perfect, but it’s better:

Sora/PC-Welt

4. Avoid text in the video

Text remains one of the biggest weaknesses of current AI video generators. While many models already achieve high visual quality in images and movements, they quickly reach their technical limits when it comes to displaying text: letters change their shape, words remain incomplete or appear as strings of characters that are difficult to decipher.

The main problems are longer texts, changing lettering, or content such as book pages, road signs, or packaging labels. The more text the AI has to display, the higher the probability of errors.

If text in the video is unavoidable, you should consciously reduce it and only use simple words or very short phrases.

5. Limit the number of objects in the image

AI video models struggle to display multiple people or objects at the same time. As the number of visible elements increases, the likelihood of errors rises significantly: faces change, bodies briefly merge, or objects appear unexpectedly and disappear.

Videos look much more stable when the action is separated in time or space. Instead of showing several people at once, focus on them one after the other. For example, the camera can pan from one person to the next, or clearly position a main character in the foreground while others remain outside the frame.

An example:

“Two people sit opposite each other, talking and gesturing, while other people walk by in the background.”

This prompt is more likely to result in distorted faces or unstable interactions. Here’s a much better example:

“One person is sitting at a table and talking. The camera initially shows only this person. Then the camera slowly pans to the second person sitting opposite. At no point are both people completely in focus at the same time.”