Dividing videos into smaller sections (or spatiotemporal patches) results

[rUparaHmaN014]

By unifying the way we represent data, we train broadcast transformers with a wider range of visual data than previously possible, spanning different durations, resolutions, and aspect ratios,” OpenAI notes.

The creation of a three-dimensional grid that is progressively filled with compressed visual data.

An associated decoding model then anhui mobile phone numbers database links the spatiotemporal patches (which are nothing more than small units that group information) to pixel data, which is ultimately used to generate realistic-looking videos.

The diffusion model that Sora relies on is fed with noisy patches (raw image fragments) and is trained to predict what those patches will look like free of any kind of noise. “Sora is a diffusion model. By receiving noisy input patches and conditioning information such as text prompts, it is trained to predict the original clean patches,” OpenAI emphasizes.

Like other great language models, Sora relies on a huge amount of data to train itself. And the videos used for training are processed at their original resolution and ratio. This allows the model to be provided with insights into the physical world and to mimic details present in people, animals and all kinds of real environments with incredible accuracy .

The deeper you go into training the OpenAI tool, the better results Sora is able to deliver. In other words, what Sora is able to achieve is largely due to the progress made in its training.

In this sense, what OpenAI has shown so far is just the tip of the iceberg of a tool that in the future could generate complete films with solid and coherent plots and specific main characters.