Evidence map›Paper›PMID 40837875›Full record

ReviewResearch (Washington, D.C.)2025

Generative Artificial Intelligence in the Metaverse Era: A Review on Models and Applications.

Han Zhou, Xinyi Chen, Jin Li, Zichen Zhang, Yao Fu, Mailyn Pérez Liva, Dov Greenbaum, Pan Hui

Abstract readReview
In one paragraph

Review in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Han ZhouSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Xinyi ChenSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Jin LiSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID https://orcid.org/0000-0001-9004-715X
Zichen ZhangNational Key Lab of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China.
Yao FuThe Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130000, China.
Mailyn Pérez LivaDepartment of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Dov GreenbaumDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Pan HuiThe HKUST-DT System and Media Lab, Hong Kong University of Science and Technology, Hong Kong 999077, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Metaverse is a decentralized, immersive 3-dimensional virtual environment that merges the physical and virtual worlds, fundamentally transforming digital interaction and garnering widespread attention. However, its primary platforms face challenges such as low-quality content and underdeveloped virtual environments, leading to a subpar user experience. Artificial intelligence-generated content (AIGC) has emerged as a key driver in Metaverse development, enabling the efficient and cost-effective creation of digital content. AIGC also promotes personalized content, further enhancing the appeal of the Metaverse. Although AIGC holds great promise, comprehensive investigations into its underlying models and applications remain limited. This study begins with the core neural network architectures of generative AI and examines the relationship between the Metaverse and AIGC. It delves into the deep learning technologies that support AIGC, providing both qualitative and quantitative analyses of their advantages, limitations, and hardware constraints. We also review existing practical applications of the Metaverse, highlighting the challenges and future opportunities in key domains such as healthcare and education. The research concludes that while AIGC can markedly accelerate the development of the Metaverse, its technology must be more closely aligned with development needs to deliver a truly immersive experience. The integration of AIGC and the Metaverse represents the convergence of artificial intelligence, computer graphics, and human-computer interaction. This interdisciplinary synergy has the potential to redefine the way we create, interact with, and experience digital environments, pushing the boundaries of creativity and immersion.

Identifiers

PMID40837875
PMCPMC12364526

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.