A video generation model predicts frames, visual representations, or motion over time using a prompt and optional references. It may generate a complete clip, animate a still image, extend footage, edit a region, or synchronize a face with speech.
Quality includes more than an attractive frame. Useful evaluation checks motion, temporal continuity, object identity, physics, prompt adherence, text rendering, dialogue synchronization, controllability, speed, and consistency across repeated generations.
ELI5
A video generation model creates or changes moving visual sequences from text, images, clips, audio, or other controls. It must keep objects, people, lighting, and motion reasonably consistent across many frames rather than producing one still picture.
For example, a user might provide an image and ask the model to animate a slow camera movement. A useful comparison checks prompt accuracy, visual consistency, duration, resolution, audio, generation speed, and the computing cost needed for repeated use.
What inputs can a video generation model use?
It can use text, images, video, masks, motion paths, depth, poses, audio, camera controls, seeds, and structured metadata.
Why can generated video look good but still fail a shot?
It may lose identity, continuity, object permanence, physics, text accuracy, prompt details, or synchronized dialogue during movement.
