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Generative AI

Text-to-Video

Generating video from a description — not image generation with more frames, because the hard part is that things must stay themselves.

Reading level: Curious
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When not to use it

  • When you need a specific shot. Plausible clip ≠ your clip, and direction is the expensive part of film.
  • For anything longer than a few seconds. Coherence degrades and no setting fixes it.
  • When physical accuracy matters. Contacts, liquids and collisions are almost right, which is worse than obviously wrong.
  • On undisclosed training data, commercially. Video provenance is murkier than images and the outputs are worth more.

Reach for something else instead

  • Image-to-video — start from a frame you chose. Far more controllable and how most real work is done.
  • Stock footage — licensed, clear, immediate.
  • Traditional VFX — for anything requiring specificity, still faster than fighting a generator.
  • Animation tools — if you need control over motion, tools that give control are the answer.

Sources & further reading

  • Ho et al. (2022), Video Diffusion Models — extending diffusion across time; where temporal consistency gets addressed directly.
  • Blattmann et al. (2023), Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models — latent video diffusion; the compute answer.
  • Brooks et al. (2024), Video generation models as world simulators — the spacetime-patch framing and the world-model claim, from the people making the claim.

Primary sources, listed so you can check the claims on this page rather than take them on trust.

Where people go wrong

  • Assuming it's image generation with more frames. Temporal consistency is a different and harder problem.
  • Prompting for motion and expecting precision. Scene description works; motion description barely does.
  • Judging on a curated demo reel. The failures are the informative part and they're not in the reel.
  • Reading physics competence as a world model. The failure cases argue against it, and that argument is live.

At a glance

FieldGenerative AI
The hard parttemporal consistency, not image quality
Breaks firstobject permanence, physics, hands
Honest lengtha few seconds
More controllableimage-to-video
Live argumentworld model or appearance model
DifficultyIntermediate
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Often compared with

Text-to-video vs. image-to-video — one invents everything including the thing you wanted; the other animates a frame you already approved. The second is a tool.