text-to-video generation consists of producing video sequences from a textual description, known as a prompt. The system interprets the text and synthesizes coherent frames, generating movement, transitions, and temporal consistency without the need for manual filming or animation. It relies on advanced generative models, usually diffusion models combined with transformer-type architectures, trained on large volumes of video and their associated descriptions.
Its importance lies in the fact that it drastically reduces the cost and time of audiovisual production, opening up video creation to people without technical knowledge of editing or animation. It is useful for idea prototyping, advertising content, animation prototypes, educational material, and visual effects.
It is worth keeping in mind some practical limitations:
- Duration: many models generate short clips, lasting a few seconds.
- Consistency: artifacts or inconsistencies between frames may appear.
- Control: adjusting specific details of the movement or the scene remains difficult.
Notable examples are Sora by OpenAI, Veo by Google and Runway Gen-3.