About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
airo 24(1):

Editorial

An Overview of OpenAI's Sora and Its Potential for Physics Engine Free Games and Virtual Reality

Download148 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/airo.5273,
        author={Zuyan Chen and Shuai Li and Md. Asraful Haque},
        title={An Overview of OpenAI's Sora and Its Potential for Physics Engine Free Games and Virtual Reality},
        journal={EAI Endorsed Transactions on AI and Robotics},
        volume={3},
        number={1},
        publisher={EAI},
        journal_a={AIRO},
        year={2024},
        month={3},
        keywords={Sora, World Model, GPT, Physics Engine, Virtual Reality, Game, Simulator, Text to Video, Video Generation},
        doi={10.4108/airo.5273}
    }
    
  • Zuyan Chen
    Shuai Li
    Md. Asraful Haque
    Year: 2024
    An Overview of OpenAI's Sora and Its Potential for Physics Engine Free Games and Virtual Reality
    AIRO
    EAI
    DOI: 10.4108/airo.5273
Zuyan Chen1, Shuai Li2,*, Md. Asraful Haque3
  • 1: Swansea University
  • 2: University of Oulu
  • 3: Aligarh Muslim University
*Contact email: shuai.li@oulu.fi

Abstract

Sora, OpenAI's latest text-to-video model, is particularly skilled at understanding the physical world, and all of the content it generates mostly consistent with the laws of physics. This indicates that Sora already has the beginnings of a world model and has the potential to become an excellent physics engine in the near future. This paper analyses and explains in detail the potential applications of Sora in physics engines and virtual reality. In addition, its advantages and disadvantages over traditional physics engines are compared based on its unique behavioural characteristics. Finally, it looks forward to the application of Sora in other fields.

Keywords
Sora, World Model, GPT, Physics Engine, Virtual Reality, Game, Simulator, Text to Video, Video Generation
Received
2024-03-01
Accepted
2024-03-05
Published
2024-03-06
Publisher
EAI
http://dx.doi.org/10.4108/airo.5273

Copyright © 2024 Z. Chen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL