Text-to-video AI is fundamentally harder than image generation because the model must maintain temporal consistency — the same subject must look coherent across every frame. Current video diffusion models solve this by learning the joint distribution of image frames conditioned on their sequence position. The result: objects and scenes remain stable for 2–4 seconds, after which coherence degrades unless the model is guided to simple camera motions.
Output Quality by Content Type
| Content type | Quality | Notes |
|---|---|---|
| Landscape / environment | Good | Best with slow camera motion |
| Abstract / artistic motion | Good | Paint, particles, fluid effects |
| Human walking / gesturing | Moderate | Gait artifacts appear after ~2s |
| Face close-ups | Moderate | Eye and mouth movement can appear uncanny |
| Complex action sequences | Challenging | Motion blur and subject drift |
| Text rendered on screen | Challenging | Letters distort across frames |
Tips for Better AI Video Prompts
- Keep prompts to one dominant subject and one motion type — complex multi-subject scenes lose coherence quickly.
- Specify camera motion explicitly: "slow push in", "static camera", or "gentle pan left".
- Add a color grade description like "warm golden hour light" or "cool cinematic teal shadows" for consistent aesthetic.
- Generated clips work best as background plates, reference material, or B-roll alongside real footage.