Improving Avatar Accuracy with Gaussian Process Regression Method in Mirror Metaverses

This paper deals with unwanted spatial distortion in virtual environments and its impact on the construction of metaverse environments that require high precision, especially in fields with specific requirements, such as medicine. At the same time, it presents the main technical factors leading to this phenomenon. The paper also emphasizes that data reliability is the first factor that needs to be analyzed and evaluated. Through a comprehensive analysis of the limitations of traditional methods and the development trend of techniques based on Artificial Intelligence (AI), a data processing method based on the Gaussian process regression method is proposed. Through experiments and result analysis, this method significantly improves data reliability, thereby enhancing the accuracy of avatar motion simulation in the virtual environment of the metaverse. Future research trends include further improvement of processing accuracy and speed; deploying on real devices; expanding the research into other factors contributing to unintended spatial distortions; exploring and applying appropriate processing techniques and technologies to enhance simulation reliability in virtual metaverse environments.