# Artificial Intelligence for the Metaverse: A Survey

arXiv: https://arxiv.org/abs/2202.10336 · HTML: https://mlsanity.com/paper/2202.10336/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 180 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 30 of 38)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Structured Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/1903.04197/) (2019)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images](https://mlsanity.com/paper/1803.00455/) (2018)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [CharBERT: Character-aware Pre-trained Language Model](https://mlsanity.com/paper/2011.01513/) (2020)
- [3D Object Proposals using Stereo Imagery for Accurate Object Class Detection](https://mlsanity.com/paper/1608.07711/) (2016)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder](https://mlsanity.com/paper/1802.02305/) (2018)
- [Deep Air Quality Forecasting Using Hybrid Deep Learning Framework](https://mlsanity.com/paper/1812.04783/) (2018)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection](https://mlsanity.com/paper/1812.06314/) (2018)
- [Relation Graph Network for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1912.00202/) (2019)
- [Adaptive Federated Learning and Digital Twin for Industrial Internet of Things](https://mlsanity.com/paper/2010.13058/) (2020)
- [Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection](https://mlsanity.com/paper/1801.03145/) (2018)
- [Frustratingly Short Attention Spans in Neural Language Modeling](https://mlsanity.com/paper/1702.04521/) (2017)
- [Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.03821/) (2019)
- [Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges](https://mlsanity.com/paper/1810.04107/) (2018)
- [Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning](https://mlsanity.com/paper/1901.04292/) (2018)
- [Visual Semantic Information Pursuit: A Survey](https://mlsanity.com/paper/1903.05434/) (2019)

## Built on by (most cited first, 25 of 25)

- [PerceptionGPT: Effectively Fusing Visual Perception into LLM](https://mlsanity.com/paper/2311.06612/) (2023)
- [MetaAID: A Flexible Framework for Developing Metaverse Applications via AI Technology and Human Editing](https://mlsanity.com/paper/2204.01614/) (2022)
- [Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions](https://mlsanity.com/paper/2209.04160/) (2022)
- [SurveyG: A Multi-Agent LLM Framework with Hierarchical Citation Graph for Automated Survey Generation](https://mlsanity.com/paper/2510.07733/) (2025)
- [AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends](https://mlsanity.com/paper/2208.10921/) (2022)
- [Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing](https://mlsanity.com/paper/2205.02764/) (2022)
- [Beyond Reality: The Pivotal Role of Generative AI in the Metaverse](https://mlsanity.com/paper/2308.06272/) (2023)
- [Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities](https://mlsanity.com/paper/2307.06687/) (2023)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [DeepSurvey-Bench: Evaluating Academic Value of Automatically Generated Scientific Surveys](https://mlsanity.com/paper/2601.15307/) (2026)
- [Mobile Edge Computing, Metaverse, 6G Wireless Communications, Artificial Intelligence, and Blockchain: Survey and Their Convergence](https://mlsanity.com/paper/2209.14147/) (2022)
- [Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses](https://mlsanity.com/paper/2210.13289/) (2022)
- [Federated Learning for Metaverse: A Survey](https://mlsanity.com/paper/2303.17987/) (2023)
- [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework](https://mlsanity.com/paper/2408.03694/) (2024)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [The Metaverse Data Deluge: What Can We Do About It?](https://mlsanity.com/paper/2206.10326/) (2022)
- [Need of 6G for the Metaverse Realization](https://mlsanity.com/paper/2301.03386/) (2022)
- [Edge AI as a Service with Coordinated Deep Neural Networks](https://mlsanity.com/paper/2401.00631/) (2024)
- [Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives](https://mlsanity.com/paper/2405.04718/) (2024)
- [Multimodal Perception System for Real Open Environment](https://mlsanity.com/paper/2410.07926/) (2024)
- [BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse](https://mlsanity.com/paper/2410.21675/) (2024)
- [Protego: Detecting Adversarial Examples for Vision Transformers via Intrinsic Capabilities](https://mlsanity.com/paper/2501.07044/) (2025)
- [SoK: A Systematic Bidirectional Literature Review of AI & DLT Convergence](https://mlsanity.com/paper/2605.10515/) (2026)

## Abstract (arXiv)

Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.