Image-to-video AI works by treating your picture as the first frame of a clip and generating the frames that could plausibly follow it. The model has learned, from a very large amount of video, how things usually move: how a head turns, how a camera pushes in. It starts from noise, and over many small steps it shapes that noise into a sequence of frames that agrees with your image and your prompt. It doesn’t understand the scene the way you do. It predicts what the next moments probably look like, and that one fact explains most of what goes right and wrong.
You don’t need the maths to make better clips, but a rough picture of the machinery helps a lot. It tells you why some start frames animate beautifully and others melt, why long clips drift, and why an end frame fixes so many problems.
The short version, step by step
- Your image and your prompt are turned into numbers the model can work with. A text encoder handles the prompt, and the image is compressed into the same internal space the model generates in.
- The model creates a block of random noise the size of the clip. Most models work on all the frames together, or on long chunks of them, rather than drawing one frame after another.
- Step by step, it removes noise. At each step it asks what this block should look like if it were a real video that starts on your image and matches your description.
- After enough steps the block is a clean, compressed clip. A decoder expands it back into full-resolution frames, and on models with sound, an audio track generated alongside it.
The commercial labs don’t publish every detail of their models. The open ones, such as Lightricks’ LTX family, and the published research follow this broad shape, and it’s a useful working model even where the specifics differ.
Why the model works on a compressed version
A ten-second clip at 1080p is hundreds of full images. Generating all of those pixels directly would be enormously slow, so video models first learn a compressed “latent” version of video, squeezed in space (fewer pixels) and in time (fewer frames). The generation happens in that compact space, and the decoder rebuilds the detail at the end.
This is one reason fine detail is fragile. Small text, distant faces and fingers take up very little room in the compressed version, so they’re the first things to come back slightly wrong. If a detail matters, make it big in the frame.
How your image steers the result
In image-to-video the start frame is held fixed while the model works. Each denoising step has to produce a clip whose first frame is your picture, and the frames after it have to stay close enough to be believable as the next moments of the same shot.
The model also looks across the whole clip at once. Most current video models are built on transformers, the same broad family of architecture as language models, and they relate every part of every frame to every other part. That’s how a face in the last frame stays connected to the face in the first.
Connected isn’t the same as identical, though. The further a frame is from your image, in time and in how much has moved, the less your image constrains it.
Why clips drift
Three things cause most drift, and all three follow from how the model works.
The first is distance from the anchor. Your image pins the first frame. By the eighth second, the model is mostly continuing its own earlier frames, so small errors add up. Faces soften. A shirt quietly changes its pattern. Shorter clips drift less, which is why a scene made of five-second shots usually holds together better than one long take.
The second is information the image doesn’t contain. If your character is facing the camera and the prompt asks them to turn around, the model has never seen the back of their head. It has to invent it, and its invention may not match what you had in mind. The same goes for a hand hidden in a pocket, the far side of a room or what’s behind a door.
The third is pattern-matching without physics. The model has seen countless people sit down, so sitting down works. It hasn’t learned physics or 3D space as rules. It has learned what video usually looks like. Unusual motion (a backflip off a moving car, a liquid poured upwards) has fewer examples to draw on, so it’s where you see objects merge, limbs bend wrong and things pass through each other.
Why an end frame helps so much
If drift comes from the model losing its anchor, the obvious fix is a second anchor. With a start and an end frame (first-last frame mode), the model is pinned at both edges and only has to invent the path between them. The middle of the clip is still generated, but it can’t wander off, because it has to arrive somewhere specific.
Keep the two frames close: the same person, the same place, a similar lens. Two very different frames force the model to invent a large transformation in the middle, and that’s where the strangeness shows. Several current models accept an end frame, including FLUX 3, Seedance 2.5, MiniMax H3, Wan 3.0, LTX 2.5 and Gemini Omni 1.1 Flash. FLUX 3 can also take up to 10 keyframes along the timeline. Our guide to I2V, T2V, V2V and the other modes covers the options.
What this means for your start frames
Once you know the model predicts motion from what it can see, a good start frame is easy to describe. It’s one that makes the next moments obvious.
- One clear subject. Crowds, clutter and busy patterns give the model many small things to keep consistent, and some of them won’t be.
- A pose that implies the motion. A hand already reaching for the cup animates better than a hand at rest that has to start from nothing.
- Room to move. Leave space in the direction of travel. A character walking towards the right edge of a tight frame has nowhere to go.
- Light with a source. A visible window or lamp gives the model a consistent logic to keep. Flat light with no direction tends to shift as the clip plays.
- No text in the image. Lettering is hard to keep stable across frames on most models, so add titles in your edit.
- The right shape and size. Make the frame in the aspect ratio of the clip you want, at a resolution at least as high as the output.
What this means for your prompt
The image has already answered every question about how things look, so the prompt only has to describe what happens. “Slow push in. She looks up from the letter and folds it once” is a complete image-to-video prompt. Describing her hair, coat and the room again doesn’t help and often hurts. The model reads it as instructions, and instructions about appearance invite it to change the appearance.
Ask for one action and one camera move. Two moves at once (“orbit and push in while panning”) pulls the generation in different directions, and it usually ends up doing neither well. For the rest of the craft, see our 10 best practices for AI video prompts.
FAQ
How does AI image-to-video technology work?
The model holds your image as the first frame and generates the rest of the clip by removing noise in many small steps, guided by your prompt and by patterns of motion it learned from a large amount of video. It predicts plausible next moments rather than simulating the scene.
Does the AI understand what is in my picture?
Only statistically. It recognises patterns it has seen many times, such as faces, hands, cars and water, and it knows how they usually move. It has no model of the actual 3D scene, which is why hidden areas and unusual motion are where errors appear.
Why does my character’s face change halfway through the clip?
The start frame constrains the early frames most. As the clip runs on, small errors add up. Use shorter clips, frame the face larger, ask for smaller motion, or add an end frame of the same character so the clip is pinned at both ends.
Why does image-to-video handle some motion so badly?
Because it has fewer examples of it. Common, physically plausible actions animate well. Rare or impossible ones push the model outside what it learned, and that’s where you see melting, merging and limbs bending wrong.
Is image-to-video better than text-to-video?
For shots that must match something, such as a character, a product or the previous shot, yes. Text-to-video is better for exploring ideas. Our text-to-video vs image-to-video comparison goes through when to use each.