Federated Multi-View Synthesizing for Metaverse

• URL: http://arxiv.org/abs/2401.00859v1
• Date: Mon, 18 Dec 2023 13:51:56 GMT
• Title: Federated Multi-View Synthesizing for Metaverse
• Authors: Yiyu Guo, Zhijin Qin, Xiaoming Tao, Geoffrey Ye Li
• Abstract summary: The metaverse is expected to provide immersive entertainment, education, and business applications. Virtual reality (VR) transmission over wireless networks is data- and computation-intensive. We have developed a novel multi-view synthesizing framework that can efficiently provide synthesizing, storage, and communication resources for wireless content delivery in the metaverse.
• Score: 52.59476179535153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is data- and computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse. We propose a three-dimensional (3D)-aware generative model that uses collections of single-view images. These single-view images are transmitted to a group of users with overlapping fields of view, which avoids massive content transmission compared to transmitting tiles or whole 3D models. We then present a federated learning approach to guarantee an efficient learning process. The training performance can be improved by characterizing the vertical and horizontal data samples with a large latent feature space, while low-latency communication can be achieved with a reduced number of transmitted parameters during federated learning. We also propose a federated transfer learning framework to enable fast domain adaptation to different target domains. Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery.

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