Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Authors: Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, Lichao Sun

What

This paper provides a comprehensive review of Sora, OpenAI’s text-to-video generation model, exploring its background, related technologies, potential applications, limitations, and future directions.

Why

Sora represents a significant breakthrough in AI, demonstrating the ability to generate high-quality, minute-long videos from text prompts, thus marking a milestone in AI-powered video generation and opening up possibilities in various fields.

How

The paper combines analysis of published technical reports and reverse engineering based on existing literature to dissect Sora’s architecture, training methodologies, and capabilities.

Result

The authors provide insights into Sora’s architecture, including data pre-processing, the use of diffusion transformers, language instruction following, and prompt engineering. They highlight Sora’s ability to handle variable video durations and resolutions, simulate complex scenes, and produce high-quality videos, while also pointing out current limitations in physical realism and human-computer interaction.

LF

The paper identifies limitations like challenges in accurately depicting complex physical interactions, maintaining temporal accuracy, and limitations in user control for detailed modifications. It suggests future research directions such as exploring more robust training datasets, improving realism in physical simulations, and enhancing user interaction capabilities for finer control over video generation.

Abstract

Sora is a text-to-video generative AI model, released by OpenAI in February 2024. The model is trained to generate videos of realistic or imaginative scenes from text instructions and show potential in simulating the physical world. Based on public technical reports and reverse engineering, this paper presents a comprehensive review of the model’s background, related technologies, applications, remaining challenges, and future directions of text-to-video AI models. We first trace Sora’s development and investigate the underlying technologies used to build this “world simulator”. Then, we describe in detail the applications and potential impact of Sora in multiple industries ranging from film-making and education to marketing. We discuss the main challenges and limitations that need to be addressed to widely deploy Sora, such as ensuring safe and unbiased video generation. Lastly, we discuss the future development of Sora and video generation models in general, and how advancements in the field could enable new ways of human-AI interaction, boosting productivity and creativity of video generation.