The rapid emergence of the Metaverse requires higher network throughput and lower latency to deliver immersive and responsive virtual experiences. Traditional centralized data processing approaches are constrained by limited computational and bandwidth resources when handling large-scale user data. A Cloud-Edge-End transmission architecture is proposed in this study, tailored for Metaverse scenarios to optimize resource allocation, minimize latency, and enhance rendering efficiency. A real-time trajectory segment prediction scheme (FDK) was developed, which combines FastDTW with K-means by leveraging user behavior trajectories to determine subscene popularity and store them on GPU servers, thereby reducing user wait time. A two-tier cache optimization scheme (MAE2C) is also proposed, incorporating GCN for subscene feature identification. GPU servers employ the MADDPG strategy to cache popular subscenes, while edge servers utilize DDPG to cache missed scenes. This approach effectively reduces cloud access and cache replacement frequency. Simulation results demonstrate that the subscene cache hit rate of the MAE2C scheme significantly outperforms existing methods across various cache capacities, with a 6.9% reduction in cache replacement frequency. This research provides effective technical support for Metaverse scene rendering and offers insights into the development of generative Metaverse systems.

With the rapid development of the Metaverse, the demand for high transmission rates and low latency is increasing. Traditional centralized data processing architectures, however, are unable to meet these demands due to resource and bandwidth bottlenecks. This paper proposes a cloud-edge-end collaborative transmission architecture to optimize resource allocation and improve rendering efficiency. A real-time trajectory segmentation prediction scheme (FDK) integrates the FastDTW algorithm with KMeans clustering to predict sub-scene popularity, enabling GPU cache allocation and reducing user wait times. Additionally, a two-tier cache optimization scheme (MAE2C) uses GCN to analyze sub-scene features, employing MADDPG to cache popular scenes on GPU servers and DDPG to cache missed scenes on edge servers. Simulation results show that the MAE2C scheme significantly improves cache hit rates, reducing cache replacement frequency by45.14%. This study provides efficient support for Metaverse scene rendering and insights for the development of generative Metaverse technologies.

With the emergence of the Metaverse concept, the rendering and transmission of 3D virtual scenes demand high-bandwidth, high-quality real-time rendering technology, as well as ultra-reliable low-latency communication (URLLC). However, the current 5G network technology faces unprecedented challenges. The 3D Gaussian Splatting technique renders realistic scenes from sparse input views, providing a novel solution to these challenges. This paper proposes a cloud-edge-end collaborative caching optimization scheme based on deep reinforcement learning and asynchronous federated learning (termed Gaussian Splatting Actor-Critic, GSAC) for 3D Gaussian Splatting rendering near terminals. This scheme leverages asynchronous federated learning technology to predict user behavior trajectories in real time while ensuring user data privacy. It integrates the ActorCritic strategy of deep reinforcement learning and graph convolutional network (GCN) technology to dynamically adjust caching strategies, thereby improving cache hit rates and reducing cache switching frequency. Simulation experiments demonstrate that compared to existing technologies, the GSAC scheme significantly outperforms in terms of target sub-scenario cache hit rates and cache switching frequency.

Abstract text summarization is a classic sequence-to-sequence natural language generation task. In order to improve the quality of unsupervised abstract text summarization in unsupervised mode, we propose two constraints for training text summarization model, embedding space constraint and information ratio constraint. We construct a generative adversarial network with two discriminators based on these two constraints (TC-SUM-GAN). We use unsupervised and supervised methods to train the model in the experiment. Experimental results show that the ROUGE-1 value of the unsupervised TC-SUM-GAN increases by [Formula: see text] points compared with the basic model and at least 1.96 points compared with other comparative models. The ROUGE scores of the supervised TC-SUM-GAN are also improved. TC-SUM-GAN achieves very competitive results for the metrics of ROUGE-1 and ROUGE-2. In addition, the abstracts generated by our model are closer to those generated manually.