The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076\% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

翻译：元宇宙作为娱乐、社交和工作平台的普及，极大催生了虚拟世界中无缝化虚拟形象集成的需求。在元宇宙中，虚拟形象必须不断更新和渲染以反映用户行为。实现虚拟分身与用户之间的实时同步极为复杂，对元宇宙服务提供商的渲染资源分配方案提出了高要求。为解决此问题，我们提出一种语义通信框架，该框架利用竞赛论对用户与MSP之间的交互进行建模，并确定每位用户的最优资源分配方案。为降低无线传输中的网络资源消耗，我们采用语义通信技术减少需传输的数据量。在我们的仿真设置下，编码后的语义数据仅包含51字节的骨骼坐标，而非原图像大小的8.243兆字节。此外，我们通过实现深度Q网络优化奖励设置，以实现最佳性能和高效资源分配。在最优奖励设置下，用户被激励选择各自合适的上传频率，相比传统平均分配方法，渲染资源约束导致的下采样损失降低了66.076%。该框架为虚拟现实环境中虚拟形象关联的资源分配提供了创新解决方案，确保所有用户获得流畅且沉浸式的体验。