This repository provides a comprehensive review paper on OpenAI's Sora text-to-video model, targeting researchers and practitioners interested in the rapidly evolving field of large vision models. It offers a structured overview of Sora's background, underlying technologies, diverse applications, current limitations, and future opportunities in generative AI for video.

How It Works

The paper synthesizes information from public technical reports and reverse engineering efforts to dissect Sora's architecture and capabilities. It categorizes key technologies, including data pre-processing, vision transformer models, diffusion models, and language instruction following techniques, highlighting their role in achieving realistic and imaginative video generation.

Quick Start & Requirements

This repository primarily serves as a reference for a research paper and does not contain executable code for Sora itself. The paper is available on arXiv: https://arxiv.org/abs/2402.17177.

Highlighted Details

• Comprehensive review of Sora, a state-of-the-art text-to-video generative AI model.
• Detailed breakdown of technologies including Vision Transformers, Diffusion Models, and instruction following.
• Exploration of applications across industries like filmmaking, education, healthcare, and robotics.
• Discussion of trustworthiness, bias, and safety considerations in large vision models.

Maintenance & Community

The paper was uploaded to arXiv on February 28, 2024, and was featured as the Daily Paper by Hugging Face. Contact is available via email: lis221@lehigh.edu.

Licensing & Compatibility

The content of the review paper is typically governed by the terms of the arXiv repository and any associated Creative Commons licenses if specified. The underlying technologies discussed are subject to their respective licenses.

Limitations & Caveats

This repository contains a review paper, not the Sora model itself. Access to and use of OpenAI's Sora model are subject to OpenAI's terms and availability. The paper relies on publicly available information and reverse engineering, which may not capture the full internal details of the model.