GFX-201c · Module 1

The AI Motion Landscape

3 min read

AI video generation in 2026 is where AI image generation was in 2023 — wildly impressive in demos, wildly inconsistent in production. Runway Gen-3, Pika, Sora, Kling, Veo 2 — the tools are multiplying faster than anyone can benchmark them. Each one does something extraordinary in its best case and something embarrassing in its worst. The landscape is fragmented, evolving weekly, and impossible to master in its entirety. Which means the skill is not mastering any single tool. The skill is understanding what all of them can and cannot do, so you choose the right one for the right job.

The current generation of video models falls into three categories. Text-to-video tools generate motion from a text prompt alone — describe a scene and the model creates a clip. Image-to-video tools animate a static image, adding motion to a composition you have already approved. Video-to-video tools transform existing footage using style transfer, re-lighting, or subject replacement. Each category has a different reliability profile. Image-to-video is the most controllable because you start from a known visual state. Text-to-video is the least controllable because the model makes every visual decision.

Do This

  • Start with image-to-video when you need brand consistency — begin from an approved static frame
  • Use text-to-video for exploration and concept development, not final deliverables
  • Test the same motion brief across two tools before committing to a workflow

Avoid This

  • Expect text-to-video to produce brand-consistent output without extensive post-processing
  • Use the longest generation duration available — shorter clips are more coherent
  • Assume a tool that produced one great clip will reliably produce the next