← ML Research Wiki / 2402.17177

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

Yixin Liu Lehigh University, Kai Zhang Lehigh University, Yuan Li, Zhiling Yan Lehigh University, Chujie Gao Lehigh University, Ruoxi Chen Lehigh University, Zhengqing Yuan, Yue Huang Lehigh University, Hanchi Sun Lehigh University, Jianfeng Gao Lehigh University Microsoft Research, Lifang He Lehigh University, Lichao Sun Lehigh University, † Lichao Lehigh University (2024)

Paper Information
arXiv ID
2402.17177
Venue
arXiv.org
Domain
artificial intelligence, computer vision, natural language processing
SOTA Claim
Yes
Contents

Abstract

Note: This is not an official technical report from OpenAI.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.

Summary

This paper provides a comprehensive review of Sora, a groundbreaking text-to-video generative AI model released by OpenAI in February 2024. It highlights Sora's development, underlying technology, applications across various industries, and the challenges and limitations it faces. Key features of Sora include its ability to generate 1-minute long videos with high visual quality based on text instructions, and its implementation of a pre-trained diffusion transformer that processes video data efficiently using latent spacetime patches. Various potential applications of Sora are discussed, including improvements in film-making, education, and marketing. Limitations such as issues with physical realism, spatial and temporal complexities, and the current restriction to 1-minute videos are also noted. The paper emphasizes the future opportunities for AI-driven video generation and Sora’s role in enhancing user creativity and productivity.

Methods

This paper employs the following methods:

  • Diffusion Model
  • Transformer

Models Used

  • Sora

Datasets

The following datasets were used in this research:

  • None specified

Evaluation Metrics

  • None specified

Results

  • Sora can generate up to 1-minute long high-quality videos based on text prompts.
  • Improves simulation abilities and enhances creative processes in various industries.

Limitations

The authors identified the following limitations:

  • Inconsistent handling of physical principles in complex scenes.
  • Temporal accuracy issues in event sequencing.
  • Challenges in user interaction and modification efficiency.

Technical Requirements

  • Number of GPUs: None specified
  • GPU Type: None specified

Keywords

large vision models diffusion transformers text-to-video generation multimodal AI video synthesis

Papers Using Similar Methods

External Resources

Funding: Not specified
References: 192
Influential Citations: 10