Core Viewpoint - The article discusses the development of HRAvatar, a method for creating high-quality, relightable 3D avatars from monocular video, addressing challenges in animation, real-time rendering, and visual realism [1][4][6]. Group 1: Methodology and Innovations - HRAvatar utilizes a learnable deformation basis and linear skinning techniques to achieve flexible and precise geometric transformations [1][6]. - An end-to-end expression encoder is introduced to enhance the accuracy of expression parameter extraction, reducing tracking errors and ensuring generalization [6][10]. - The method decomposes the avatar's appearance into material properties such as albedo, roughness, and Fresnel reflectance, employing a simplified BRDF model for shading [6][16]. Group 2: Performance and Results - HRAvatar demonstrates superior performance across various metrics, achieving a PSNR of 30.36, MAE of 0.845, SSIM of 0.9482, and LPIPS of 0.0569, outperforming existing methods [24][26]. - The method achieves real-time rendering speeds of approximately 155 FPS under driving and relighting conditions [25]. - Experimental results indicate that HRAvatar excels in detail richness and quality, particularly in LPIPS scores, suggesting enhanced avatar detail [24][34]. Group 3: Applications and Future Directions - The reconstructed avatars can be animated and relit in new environmental lighting conditions, allowing for simple material editing [28]. - The introduction of HRAvatar expands the application scenarios for monocular Gaussian virtual avatar modeling, with the code being open-sourced for public use [35][36].