Text-to-video is a mode of AI generation in which a written text prompt is used as the primary input to generate a video clip, with the model synthesizing motion, subject behavior, camera movement, and temporal progression from the language of the prompt alone. It extends the text-to-image paradigm into the time dimension, requiring the model to generate not just a single coherent frame but a sequence of frames with consistent, plausible motion and visual continuity.

Text-to-video is technically more demanding than text-to-image generation because the model must maintain consistency across many frames while also generating believable motion, physics, and temporal progression. The prompt must communicate not just what should be visible but how things should move and change over time - a description that reads clearly as a static scene often needs additional motion and action language to translate effectively into video. Leading text-to-video models have developed strong capabilities for certain types of content such as natural environments, simple subject actions, and atmospheric scenes, while complex multi-character interactions, precise physical interactions, and very long clip durations remain more challenging.

Text-to-video is the primary generation mode on Morphic, with multiple video generation models available to interpret prompts. Writing effective text-to-video prompts involves describing not just the visual scene but the action, movement, and progression within it - specifying what changes over time, how the camera moves, and what happens from the beginning to the end of the clip produces more dynamic and purposeful results than describing static scenes.