GFX-201c · Module 1

Temporal Consistency

3 min read

Temporal consistency is the single biggest challenge in AI-generated video. It means that a subject should look the same in frame 1 as it does in frame 120. The same face. The same clothing. The same color palette. The same lighting direction. Current models achieve this sometimes — and when they do, the result is mesmerizing. When they fail, the result is a shape-shifting nightmare that breaks the illusion in the first second.

The techniques for maintaining temporal consistency mirror the techniques for maintaining brand consistency in static images, which is not a coincidence. Both are fundamentally about constraining variation across a series of related outputs. Reference images anchor the visual identity. Short generation durations limit the window for drift. Consistent prompts eliminate the variability that comes from rewriting descriptions. The discipline is the same — only the dimension changes from "across images" to "across frames."

Do This

  • Use image-to-video instead of text-to-video when consistency matters — the starting frame anchors everything
  • Generate shorter clips (2-4 seconds) and edit them together rather than one long generation
  • Lock seeds and parameters between regeneration attempts to maintain visual continuity

Avoid This

  • Generate 10+ second clips and expect consistent subject appearance throughout
  • Mix text-to-video outputs from different prompts and expect them to cut together seamlessly
  • Ignore frame-by-frame inspection — temporal artifacts are invisible at normal playback speed until your audience notices them