This paper presents a novel method for reconstructing vivid 3D Gaussian avatars from monocular videos (GVA). The method addresses the challenges of high-fidelity human body reconstruction and accurate alignment of 3D Gaussians with human skin surfaces. Key contributions include a pose refinement technique to improve hand and foot pose accuracy by aligning normal maps and silhouettes, and a surface-guided re-initialization method to alleviate unbalanced aggregation and initialization bias. The proposed method achieves high-fidelity and vivid 3D Gaussian avatar reconstruction, with extensive experiments validating its performance in photo-realistic novel view synthesis and fine-grained control over the human body and hand pose. The method is evaluated on multiple datasets, demonstrating superior accuracy and efficiency compared to existing approaches. The results show that the proposed method outperforms other methods in terms of PSNR, SSIM, and LPIPS metrics. The method also shows improved performance in handling complex scenarios such as long-haired shawls and loose clothing. The method is able to reconstruct avatars with accurate body and hand poses, and it is capable of handling a wide range of pose variations. The method is also able to handle challenging scenarios such as sideways positioning and complex hand movements. The method is able to generate realistic avatars with high fidelity and accurate pose control. The method is able to handle a wide range of pose variations and is capable of generating realistic avatars with high fidelity and accurate pose control. The method is able to handle a wide range of pose variations and is capable of generating realistic avatars with high fidelity and accurate pose control.This paper presents a novel method for reconstructing vivid 3D Gaussian avatars from monocular videos (GVA). The method addresses the challenges of high-fidelity human body reconstruction and accurate alignment of 3D Gaussians with human skin surfaces. Key contributions include a pose refinement technique to improve hand and foot pose accuracy by aligning normal maps and silhouettes, and a surface-guided re-initialization method to alleviate unbalanced aggregation and initialization bias. The proposed method achieves high-fidelity and vivid 3D Gaussian avatar reconstruction, with extensive experiments validating its performance in photo-realistic novel view synthesis and fine-grained control over the human body and hand pose. The method is evaluated on multiple datasets, demonstrating superior accuracy and efficiency compared to existing approaches. The results show that the proposed method outperforms other methods in terms of PSNR, SSIM, and LPIPS metrics. The method also shows improved performance in handling complex scenarios such as long-haired shawls and loose clothing. The method is able to reconstruct avatars with accurate body and hand poses, and it is capable of handling a wide range of pose variations. The method is also able to handle challenging scenarios such as sideways positioning and complex hand movements. The method is able to generate realistic avatars with high fidelity and accurate pose control. The method is able to handle a wide range of pose variations and is capable of generating realistic avatars with high fidelity and accurate pose control. The method is able to handle a wide range of pose variations and is capable of generating realistic avatars with high fidelity and accurate pose control.