Sora is OpenAI’s text-to-video model that takes a user’s textual prompt (and optionally image or video input) and produces a short video clip matching that description. Under the hood, Sora treats video generation as a diffusion process: it initializes a latent video (essentially noise) and then iteratively denoises it toward a coherent visual output. According to OpenAI, the model starts “with a base video that looks like static noise and gradually transforms it by removing the noise over many steps.”
To maintain consistency across frames, Sora represents video as spatio-temporal patches (small 3D blocks spanning space and time) and uses a transformer architecture over those patches. This lets the model reason about how objects move, occlude, or reappear across frames. The diffusion component is responsible for filling in fine visual detail, while the transformer component helps structure the global layout and maintain coherence of motion and objects. The training also uses a “recaptioning” or prompt enrichment step: the user prompt is expanded or refined by a language model, which gives the generation model more guidance in building detailed scenes.
In summary, Sora combines diffusion + transformer modeling in a latent video space to map text to motion. While it can produce visually compelling short clips, it has limitations: complex scenes, subtle motion, or physical dynamics can lead to artifacts or prompt mismatch. But conceptually, Sora frames video generation as patchwise diffusion with cross-frame attention, making it a scalable approach to text-to-video.