In this paper, we address the issues of low training efficiency and insufficient pose generalization ability in personalized 3D human avatar creation using neural radiation fields based implicit modeling techniques. We propose a novel method that combines 3D Gaussian splatting with parametric human models to provide an explicit representation. Additionally, we introduce a Point Transformer architecture based on attention mechanisms. This architecture can deeply learn and extract pose information from each frame and effectively integrate it into the Gaussian attribute parameters, thereby enhancing the rendering capabilities of the model. In experiments conducted on the People-Snapshot dataset, our method is compared with current state-of-the-art methods. Quantitative results show that our approach achieves an average PSNR of 29.53, which is a 13.7% improvement over the baseline method, demonstrating a significant advantage. Qualitative evaluations indicate that even with large avatar movements, our algorithm can effectively maintain the integrity and detail of the rendering results.