# Generative AI for Immersive Communication: The Next Frontier in Internet-of-Senses Through 6G

arXiv: https://arxiv.org/abs/2404.01713 · HTML: https://mlsanity.com/paper/2404.01713/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [Language Modeling Is Compression](https://mlsanity.com/paper/2309.10668/) (2023)
- [MegaBlocks: Efficient Sparse Training with Mixture-of-Experts](https://mlsanity.com/paper/2211.15841/) (2022)
- [TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones](https://mlsanity.com/paper/2312.16862/) (2023)
- [TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge](https://mlsanity.com/paper/2310.15051/) (2023)
- [MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets](https://mlsanity.com/paper/2308.12067/) (2023)
- [FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions](https://mlsanity.com/paper/2305.17718/) (2023)
- [YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs](https://mlsanity.com/paper/2110.13713/) (2021)
- [YOLO-based Semantic Communication with Generative AI-aided Resource Allocation for Digital Twins Construction](https://mlsanity.com/paper/2306.14138/) (2023)
- [On the Diagnostic of Road Pathway Visibility](https://mlsanity.com/paper/1601.05535/) (2016)
- [WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery](https://mlsanity.com/paper/2211.01241/) (2022)
- [Mobile Edge Generation: A New Era to 6G](https://mlsanity.com/paper/2401.08662/) (2023)
- [Mulsemedia Communication Research Challenges for Metaverse in 6G Wireless Systems](https://mlsanity.com/paper/2306.16359/) (2023)
- [Dynamic and Super-Personalized Media Ecosystem Driven by Generative AI: Unpredictable Plays Never Repeating The Same](https://mlsanity.com/paper/2402.12412/) (2024)

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [WDMoE: Wireless Distributed Mixture of Experts for Large Language Models](https://mlsanity.com/paper/2411.06681/) (2024)
- [Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey](https://mlsanity.com/paper/2504.07635/) (2025)

## Abstract (arXiv)

Over the past two decades, the Internet-of-Things (IoT) has become a transformative concept, and as we approach 2030, a new paradigm known as the Internet of Senses (IoS) is emerging. Unlike conventional Virtual Reality (VR), IoS seeks to provide multi-sensory experiences, acknowledging that in our physical reality, our perception extends far beyond just sight and sound; it encompasses a range of senses. This article explores the existing technologies driving immersive multi-sensory media, delving into their capabilities and potential applications. This exploration includes a comparative analysis between conventional immersive media streaming and a proposed use case that leverages semantic communication empowered by generative Artificial Intelligence (AI). The focal point of this analysis is the substantial reduction in bandwidth consumption by 99.93% in the proposed scheme. Through this comparison, we aim to underscore the practical applications of generative AI for immersive media. Concurrently addressing major challenges in this field, such as temporal synchronization of multiple media, ensuring high throughput, minimizing the End-to-End (E2E) latency, and robustness to low bandwidth while outlining future trajectories.