Design of an Integrated Model Combining CycleGAN, PPO, and Vision Transformer for Adaptive Scene Rendering in the Metaverse

The emergence of the metaverse demands adaptive rendering systems that produce high-quality scenes, balancing the dimensions of visual fidelity with computational efficiency. Methods used for scene generation and rendering usually fail to incorporate user interactions in real time, leading to resource inefficiency and reduced user experience. This paper proposes a new, all-in-one model that integrates CycleGAN, Proximal Policy Optimization (PPO), Vision Transformer (ViT), Bootstrap Your Own Latent (BYOL), and Adaptive Feedback Learning (AFL-RT) in an attempt at dynamic optimization of the scene rendering based on user behavior and environmental data. The CycleGAN module ensures the production of the high-resolution scenes even from low-resolution inputs, whereas PPO provides for continuous adaptation of rendering parameters so as to reduce latency and computational load. ViT uses attention-based rendering, providing better visualization to user-centric areas, and BYOL does self-supervised scene understanding and compression, maintaining important information though reducing the bandwidth usage. AFL-RT is actually a closed-loop feedback system used to refine rendering parameters in real-time by actually reflecting the user’s preference and interests. Experimental results demonstrate that the proposed model reduces latency by as much as 30% in rendering, improves visual fidelity by up to 15%, and decreases energy consumption by 25% compared with the baseline methods. The user satisfaction also increases up to 25% regarding real-time adaptations by using this model, which only validates the practicality of such a model for dynamic, interactive metaverse environments. This work offers a comprehensive solution to the challenges of adaptive scene rendering, thus highly applicable to future metaverse applications in almost all industries and scenarios.