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How Generative AI Creates Text, Images, and Videos from a Prompt

1The Shared Idea Behind All Generative AI2How a Prompt Becomes an Instruction3Generating Text: One Token at a Time4Generating Images: From Noise to Picture5Generating Video: Adding Time6Comparing the Three Modalities and Judging Outputs
Generating Video: Adding Time

Why Longer Clips Fall Apart

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Follow the dog as the clip plays. In the first second the frames agree closely, so the motion looks clean. Now watch the later frames: a tiny mismatch between two neighbors gets copied into the next frame, and the next, and the gap widens. That one mechanism shows up four ways. Flicker is the small version, a shimmer in brightness or texture. Morphing is the object itself changing. Drift is the whole scene sliding or shifting color. Warping is the subject bending under a motion it cannot satisfy. Short clips stay steady; long ones fall apart, because the model is only making each frame look locally plausible, not tracking the dog the way you would.
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Consistency is not a fixed cost. It is a requirement that has to hold between every pair of neighboring frames, so the number of agreements the model must maintain grows with the length of the clip. A two-second clip asks for a few dozen consistent transitions; a twenty-second clip asks for hundreds. Each one is a chance for a small disagreement to creep in, and small disagreements do not stay small — once a frame drifts slightly from the one before it, the next frame is refined against the drifted version, and the error compounds down the sequence.

This is why short clips are easier than long ones, and it is a structural reason rather than a limitation of any particular product. The same effect explains the characteristic artifacts.

Flicker is the mildest form: brightness, color, or fine texture shifts slightly from frame to frame, so the clip appears to shimmer even when nothing in the scene is moving. It usually comes from tiny frame-to-frame disagreements in pixel values that the eye picks up as a pulse.

Morphing is more serious. An object gradually changes shape or identity across the clip — a face slowly becomes a different face, a hand gains or loses fingers, a logo's letters rearrange. This is the temporal version of the alignment failures seen with still images, except that here the model has no single frame to get right; it has to keep getting the object right while the object also moves.

Drift is the slow version of the same problem: the whole scene slides, the subject's color shifts, or the background gradually changes, so the clip no longer looks like one continuous shot. Warping appears when the model tries to reconcile a moving object with a constraint it cannot satisfy, and the object bends or stretches unnaturally in the attempt.

All four artifacts share one cause. The model is not tracking objects the way a person does; it is producing pixel values that look locally plausible, and local plausibility does not guarantee that frame fifty still depicts the same thing as frame one.

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