What is temporal consistency in AI video?

A camera records one world, so its frames agree by nature. A model generates each frame, and nothing in the physics of the process forces frame 90 to agree with frame 1. Consistency has to be learned.

Today’s video models get much of it by generating all the frames together: their attention lets every part of every frame look at every other, across space and time. Older methods that generated or edited one frame at a time flickered badly for exactly this reason. The longer the clip, the more there is to hold together, which is one reason most models make clips of seconds, not minutes.

Models that extend a clip piece by piece, or generate it frame after frame, only see part of what came before. Drift builds up at each join.

Under the hood: one clip, one shared view

The report on the open Wan model describes the usual design. A video encoder compresses the clip 4 times in time and 8 times in each direction of space, and a diffusion transformer works on the whole compressed clip at once, so every frame is generated with every other in view.

Researchers measure the result. Benchmarks such as VBench split video quality into separate scores, with temporal flickering and keeping a subject’s identity among them.

The hardest brief for consistency changes everything around a subject while asking the subject to stay exactly the same. This one, made on DreamBench with Seedance 2.5, keeps one dancer in one unbroken 15-second orbit while the stage turns into a forest and then a pool.

• Check the subject at the moments of change: the turns where one scene becomes the next.
• Pause on the hands and the edge of the fabric: fine, fast-moving detail is where drift shows first.
• Compare the first and last frames side by side: slow drift is invisible in motion and obvious in stills.

• Give the model a picture: image to video fixes the first frame, so there is less to invent.
• Use reference pictures of a character or product in every shot that shows them.
• Ask for less motion: slow camera moves and simple actions hold together better than whip pans and fights.
• Keep clips short and cut between them, as films do, instead of one long take.
• Describe the same character with the same words in every prompt.
• Avoid busy patterns, small text and crowds where you can.

When you edit a real video with AI, the original footage is already consistent; the risk is that the change isn’t. A swapped face that slides, a removed object that ghosts back or a restyle that boils from frame to frame are all failures of temporal consistency in the edit.

Why does my restyled video boil or shimmer? A restyle keeps the outlines, depth and motion of every frame and repaints their surface, and the surface is what gets regenerated slightly differently each frame: grain, brush strokes and fine patterns swim while the shapes hold. Ask for a flatter style with less texture, keep the clip short, and compare the result with the original before you use it; the original footage was steady, so any shimmer is the edit’s. A removed object that ghosts back is the same failure in a filled region.

DreamBench writes every shot of a film with the same description of each character, and the pictures you add are sent as references with every shot that needs them. In Morph, the original clip’s motion and timing are kept, so the edit follows the footage.