RECORD
Federated Multi-View Synthesizing for Metaverse
Image and Video Processing · Submitted 18 Dec 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The metaverse is expected to provide immersive entertainment, education, and business applications. | background 1.00 | 0.03 | 0.02 | hedged possibility 0.85 |
| s1 | 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. | background 0.86 | 0.06 | 0.02 | asserted 1.00 |
| s2 | 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. | contribution 0.95 | 0.92 | 0.14 | asserted 1.00 |
| s3 | We propose a three-dimensional (3D)-aware generative model that uses collections of single-view images. | method 0.76 | 0.87 | 0.03 | asserted 0.99 |
| s4 | 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. | method 0.92 | 0.63 | 0.05 | asserted 0.99 |
| s5 | We then present a federated learning approach to guarantee an efficient learning process. | method 0.51 | 0.87 | 0.04 | asserted 0.98 |
| s6 | 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. | method 0.89 | 0.81 | 0.09 | asserted 0.73 |
| s7 | We also propose a federated transfer learning framework to enable fast domain adaptation to different target domains. | contribution 0.82 | 0.89 | 0.04 | asserted 0.93 |
| s8 | Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery. | result 0.96 | 0.95 | 0.85 | asserted 1.00 |
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The paper
Original 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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What this paper does with the arXiv papers it cites
What it cites · 6 sentences without a confident label
Uses the method or tool of 1 paper
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We apply Fréchet Inception Distance (FID) [ref] and Kernel Inception Distance (KID) [ref] as identified Metrics.
Demystifying MMD GANs · Simulation Settings · 0.97
Cites as background 3 papers
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The self-supervised monocular scene reconstruction NeRF model developed by Cao et al. [ref] is trained from multiple image sequences with poses, geometry constraints, and a novel probabilistic sampling strategy.
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields · Introduction · 0.84
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Li et al. [ref] have designed a robust and adaptive metric for estimating redundancy and trainable vector quantization to improve the compactness of grid models.
Compressing Volumetric Radiance Fields to 1 MB · Related Works · 0.97
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Additionally, generative adversarial networks (GAN) have recently extended their capabilities to 3D settings, which has gained momentum as well [ref] .
StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis · Related Works · 0.98
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