  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.
