# AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends arXiv: https://arxiv.org/abs/2208.10921 ยท HTML: https://mlsanity.com/paper/2208.10921/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 82 of its 322 references that are in the map. - First posted on arXiv 23 Aug 2022. ## Builds on (most cited first, 30 of 82) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014) - [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021) - [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021) - [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018) - [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018) - [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016) - [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022) - [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020) - [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018) - [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019) - [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016) - [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020) - [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera](https://mlsanity.com/paper/1705.01583/) (2017) - [Semantic Flow for Fast and Accurate Scene Parsing](https://mlsanity.com/paper/2002.10120/) (2020) - [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019) - [Exploiting temporal information for 3D pose estimation](https://mlsanity.com/paper/1711.08585/) (2017) - [FaPN: Feature-aligned Pyramid Network for Dense Image Prediction](https://mlsanity.com/paper/2108.07058/) (2021) - [TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models](https://mlsanity.com/paper/2102.07988/) (2021) - [All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda](https://mlsanity.com/paper/2110.05352/) (2021) - [A survey on Semi-, Self- and Unsupervised Learning for Image Classification](https://mlsanity.com/paper/2002.08721/) (2020) - [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022) - [6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/1909.11315/) (2019) - [Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention](https://mlsanity.com/paper/2201.01615/) (2022) ## Built on by (most cited first, 8 of 8) - [ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review](https://mlsanity.com/paper/2305.03123/) (2023) - [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023) - [When Generative Artificial Intelligence meets Extended Reality: A Systematic Review](https://mlsanity.com/paper/2511.03282/) (2025) - [FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer](https://mlsanity.com/paper/2307.09020/) (2023) - [Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization](https://mlsanity.com/paper/2407.14573/) (2024) - [Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse](https://mlsanity.com/paper/2408.14416/) (2024) - [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024) - [Equitable Multi-Task Learning for AI-RANs](https://mlsanity.com/paper/2603.08717/) (2026) ## Abstract (arXiv) Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both in realizing the immersive experiences of Metaverse. Therefore, in this survey, we first introduce the background and ongoing progress in augmented reality (AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed by the technical aspects of AI and 6G. Then, we survey the role of AI in the Metaverse by reviewing the state-of-the-art in deep learning, computer vision, and Edge AI to extract the requirements of 6G in Metaverse. Next, we investigate the promising services of B5G/6G towards Metaverse, followed by identifying the role of AI in 6G networks and 6G networks for AI in support of Metaverse applications, and the need for sustainability in Metaverse. Finally, we enlist the existing and potential applications, usecases, and projects to highlight the importance of progress in the Metaverse. Moreover, in order to provide potential research directions to researchers, we underline the challenges, research gaps, and lessons learned identified from the literature review of the aforementioned technologies.