Text-to-video AI converts written descriptions into moving images using deep learning models trained on millions of video-text pairs. These models understand spatial relationships, motion physics, lighting, and cinematic composition.
The quality of your output depends largely on your prompt. A good prompt includes the subject, action, setting, lighting, camera movement, and style. For example: "A golden retriever running through autumn leaves in a park, slow motion, warm sunlight, shallow depth of field, cinematic."
Different models excel at different things. Wan 2.2 produces the best cinematic quality with complex camera movements. LTX-Video is the fastest for quick iterations. Kling 3.0 and Veo 3.1 generate native audio alongside the video.
Negative prompts help exclude unwanted elements. Common negative prompts include "blurry, distorted, watermark, low quality, text, oversaturated."