Why Characters and Objects Drift Between Frames

You make an AI clip of a woman in a red jacket walking down a street. At the start she has short dark hair and a small earring. Six seconds later her hair is a bit longer, the earring is gone, and a shop sign behind her has turned into a different shop. Nobody asked for those changes.

This is called drift. This lesson explains why it happens, using what you learned about how video models work.

What You'll Learn

• What temporal consistency means in practice
• The main kinds of drift you will see in AI video
• Why a model has no fixed memory of a character
• Why things change when they leave the frame and come back
• Why drift gets worse in longer and joined clips

Temporal consistency, in plain words

In a real video, the world does not change unless something changes it. A cup stays a cup. A person keeps the same face. A tree in the background stays where it is.

An AI video model has no world behind the picture. It only has the frames it is making. It has to produce the look of a stable world, frame after frame, from learned patterns. Temporal consistency is how well it manages that. When it fails, you get drift.

The common kinds of drift

Drift shows up in a few typical ways:

• Identity drift. A face slowly changes. Eye shape, nose, age, or skin tone shift over the clip.
• Clothing and detail drift. Buttons appear or vanish. A logo on a shirt changes shape. Jewelry comes and goes.
• Object drift. A mug becomes a glass. A dog's breed changes partway through.
• Background drift. Windows move. Signs rewrite themselves. A street grows or loses a building.
• Count drift. Three birds become four. A hand shows six fingers for a moment.

Small, fine details drift the most. Large, simple shapes, like the sky or a wall, usually stay steady.

Why there is no fixed memory

When you picture a friend, you hold one stable idea of that person. You know they have a scar on their chin even when you cannot see it.

A video model does not store a character like that. There is no saved file that says "this woman, this face, this earring." The character exists only as patterns spread across the frames being denoised. To keep her the same, the model has to compare each part of the clip with the other parts and keep them in agreement.

That comparing has limits:

• The model may give less weight to frames far apart in time.
• Fine details take up only a tiny part of the squeezed latent space, so they are easy to lose.
• Many details would all look "reasonable" to the model, so a small change does not look like an error to it.

The result is a character who stays roughly the same but slowly wanders in the details.

Out of view, out of mind

One of the hardest cases is when something leaves the frame and comes back. A person walks behind a pillar. The camera turns away and then turns back.

When the person comes back, the model has to recreate them. If the frames where they were visible are far away, or the view is now different, the new version may not match. This is why a character can look like a slightly different person after walking past the camera.

Why longer clips drift more

Most tools make a clip in one go, as you saw in lesson 1. But to get a longer video, many tools extend a clip. They take the last part of one clip and use it as the starting point for the next part.

Each new part only sees the end of the last one. If clip 2 changes a small detail, clip 3 treats that change as normal and may add its own. Over several extensions, the small errors add up. This is like a message passed down a line of people. Each person repeats what they heard, and the message drifts.

The same thing happens across separate shots. If you make shot A and shot B with two prompts, the model has no idea they show the same character. Each shot is made from scratch, so the person can look different in each one.

How tools try to reduce drift

Tool makers know about this problem. The general methods are:

• Reference images. You give the model a picture of the character or product. It uses that image as a guide in every frame, which gives it something like a stable memory.
• Starting from an image. In image-to-video, the first frame is fixed, so the clip starts from a known look.
• Bigger context. Newer models can compare more of the clip at once, which helps details stay steady for longer.
• Better training data. Clips with consistent, well captioned subjects teach better habits.

These help a lot, but none of them fully solve drift. Lesson 4 looks at how references and prompts steer the model in more detail.

What this means for you

When you watch AI video, or make it, expect drift. Check:

• Faces and hair at the start and end of the clip
• Small items like jewelry, buttons, logos, and signs
• Anything that leaves the frame and comes back
• Characters across different shots

Knowing where drift hides makes it easier to spot, which helps both when you judge your own clips and when you judge a clip someone else shared.

Key Takeaways

• Drift is when faces, clothes, objects, or backgrounds change between frames without reason.
• A video model has no fixed memory of a character. It keeps things the same only by comparing parts of the clip.
• Small details drift more than large, simple shapes.
• Things that leave the frame and return often come back changed, because nothing exists off screen for the model.
• Extended clips and separate shots drift more, since each part only sees part of what came before.
• Reference images and image-to-video reduce drift, but do not remove it.