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Why AI videos glitch, and how to keep them consistent

AI videos glitch because every clip is a fresh sample, and ten seconds asks the model to keep dozens of things true at once. Here is what the research measures, what causes it, and what reduces it.

Updated 8 October 2026Product details last verified 30 September 2026

The answer in one paragraph

AI videos are inconsistent because a video model does not remember a scene. It samples a new clip from noise every time, guided by your words and your reference images. Within those few seconds it has to keep every face, object and screen the same while making them move. Where the instructions and the starting picture disagree, the model settles it on its own. It morphs one object into another, lets a person appear, or hands a line to the wrong mouth. The errors are common, measurable, and mostly caused by inputs you can control.

How common it is, in numbers

The failure is not an occasional glitch. It is the normal state of the technology in 2026, and researchers have counted it.

  • A study of 600 clips from three current generators, including Google’s Veo 3, found 1,604 errors. Of those 600 clips, 484 had three or more. Objects appearing or disappearing was the third most frequent error type, after not following the prompt and breaking physics.
  • VBench-2.0, a benchmark of 18 capabilities, reports that models keep a single object intact more than 90% of the time. But when something has to change over time, such as a colour, a position or an order of events, models fail “about 80% of the time”. Clips that must tell a story in several steps score under 12 out of 100.
  • A survey of eight commercial generators concluded that “generating multiple results with the same input varies widely”. Google’s own documentation says the seed parameter “doesn’t guarantee determinism, but slightly improves it”.

So a clip that looks right on the first try is a good sample, not a solved problem. The next sample from the same inputs can differ. What you can change is how often the good sample comes up.

Five causes you can see in the clip

Each visible symptom has a cause in the inputs. Here they are, in the order they usually bite.

  1. A starting frame that lacks something the clip needs. Most tools animate from a still image. If the script hands over a printout at second eight and no printout is in the frame, the model has to invent one. It makes one from whatever is nearest: a laptop closes and becomes paper. The same goes for people. A second speaker who is off-frame at the start will walk in, cut in, or be invented with a new face.
  2. Too many changes in one clip. Four lines, two speakers, a screen that changes twice and a handover is a lot for ten seconds. The benchmark data above says changes over time are where models fail most. Each extra timed event is another roll of the dice.
  3. Two people talking. One speaker per clip is reliable. Two speakers in the same shot is where the wrong lips move, or a line becomes a voice-over. Community guides for Veo agree on this, and our own tests do too.
  4. A screen pointed at the camera. A phone or laptop shown to the viewer is unnatural staging, and the model answers by changing what is on it, turning it, or filling it with lettering. Screens that face their user stay still.
  5. A prompt built from prohibitions. Google’s prompt guide for Veo advises against “no” and “don’t” and recommends describing what you do want instead. A model reads words, not negations. “No cuts” still puts the word “cuts” in front of it.

What reduces it, with evidence

Fix What the evidence says Effort
Put everything in the first frame In our tests, every clip from a frame missing a prop either conjured it or skipped the action (six of six). From a complete frame, none did Low: check the still before you render
Fewer timed changes per clip The 80% failure rate above is specific to things that change over time Low: cut the script
One speaker per clip Lip sync held on all lines in five of seven two-speaker clips we rendered. One-speaker clips rarely miss Low
Describe, do not forbid Google’s official prompt guidance Low: rewrite
Render several takes and pick one Choosing the best of five with a trained judge beats a random take on the standard benchmark Medium: costs credits
Automated checks on each clip Vision models catch about half the errors humans catch, so narrow checks (face count, line timing) work better than “is this good?” High: a pipeline

The first two fixes are the cheapest and the most effective. A frame that already contains every person and every object the scene will use removes the main reason the model invents things. A script with one state change per two seconds, instead of three, gives it less to lose.

How this works in GetReelCom

GetReelCom builds every scene in two steps, and the split exists for this reason. First it draws a still, the preview frame, from your script, your cast’s avatars and your venue photo. You look at that frame before any video is rendered. Someone missing, a laptop facing the wrong way, the prop for the last beat not on the table: you fix the script and redraw the frame. A frame costs a fraction of a clip. Only when the frame is right does the clip start from it, in one continuous shot, with the same faces and the same objects. The help centre explains the frame step in The preview frame.

Characters are the other half. Each person in your Director’s Guide has an avatar, drawn once and sent as a reference image with every scene. That is why the same colleague looks the same in scene one and scene three. See Character avatars.

What you still decide is the script. The app drafts it as timed blocks and lines. Keep one person speaking per block where you can. Open with everyone in shot, and give the last second and a half nothing new to do. The guide to writing it is Write the scene and narration.

A worked example

A single-shot scene with one speaker, a screen that faces its user, and every object present from the first frame.

The clip above is scene two of a three-scene explainer for Triagen, which makes return-to-work software. Look at what the frame contains before anything moves: the employee, the mascot, the desk, and the screen that will show the plan. The script changes one thing per beat. The plan appears on the screen as the result of the conversation, the mascot reacts, the employee settles. Nothing has to be invented mid-clip, so nothing is.

Compare that with a scene we debugged in October 2026 for a dietitian’s practice. The script had a client sighing over a notebook and a dietitian tapping a plan on her laptop. The client reads along, and a printout is handed over at the end. The first block mentioned the dietitian’s laptop but not the dietitian. The frame came back with the client alone, the laptop facing away, and no printout. The clip then cut at two seconds to bring the dietitian in with an invented face. It turned the notebook into a second laptop, and that laptop into the printout at eight seconds. We rendered it three more times from the same frame. All three morphed the notebook. Two conjured the printout and one skipped the handover. With a frame that held both people, the laptop facing the dietitian and the printout on the desk, the morphs stopped.

Cost and time

A preview frame in GetReelCom renders in about 30 seconds and costs one credit. A ten-second scene takes about a minute and costs ten credits. Checking the frame before rendering therefore costs a tenth of a wasted clip, and most problems in a clip are visible in its frame. A second take of a scene costs the same as the first, so three takes of one scene cost what a 30-second video costs. Budget takes for the scenes that matter most, usually the opening and the one with the product on screen.

Common mistakes

  • Rendering a clip from a frame you have not looked at. The frame is where missing people and props show up for free.
  • Asking one clip to carry the whole story. Split problem, solution and result into three scenes of ten seconds each.
  • Two speakers in one shot when one would do. Give the second person the next scene.
  • A screen turned to the camera. Let the viewer watch over a shoulder or from the side.
  • Fixing a bad take with a longer prompt. Every added sentence is one more thing the model can weigh against another. Remove a change instead.
  • Trusting one good take. If a scene matters, render it twice and compare.

Frequently asked questions

Why are AI videos so inconsistent?

Because a video model samples each clip fresh and has no memory of the scene. It balances your words, your reference images and its training, and resolves conflicts between them on its own. Research on 600 clips from current generators found an average of more than two errors per clip. Inconsistency is the baseline, and inputs decide how often it shows.

Why do objects in an AI video morph or disappear?

Usually because the script needs something the first frame does not contain, or contains in the wrong state. The model cannot bring an object in from outside the picture, so it turns an existing object into the missing one. A frame that holds every prop the scene will use, placed where it is first needed, removes the cause.

How do you keep characters consistent in an AI video?

Give the model a reference image of each person and send it with every scene. Keep each character’s clothing and description identical across scenes. Put every character in the first frame, because a person who enters mid-clip is drawn without the reference. In GetReelCom this is what avatars do.

Does a longer, stricter prompt make an AI video more consistent?

Past a point, no. Google’s prompt guide for Veo advises describing what you want instead of listing what you forbid. Long prompts give the model more instructions to trade off against each other. Cut a timed change from the script before you add a rule to the prompt.

Will a seed make the same prompt give the same video?

Not reliably. Google’s documentation for Veo states that a seed “doesn’t guarantee determinism, but slightly improves it”. The Gemini Omni video model exposes no seed at all. Treat each render as a new sample and keep the ones you like.

How many takes does a usable AI clip need?

For a simple scene with one speaker and a complete frame, often one. For a scene with two speakers or a screen that must change, plan on two or three and pick the best. Benchmarks show that choosing the best of five takes beats a random take, and the frame check reduces how many takes you need.

Sources

External figures were checked on 8 October 2026.

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