Creating animatable 3D avatars from monocular videos is a promising topic with broad applications in the virtual realm. Recent 3D Gaussian splatting methods have shown advantages in training and inference compared to previous studies. However, they either zero the opacity or learn the background color at an inaccurate initial position. In this paper, we propose a Gaussian human avatar framework that decouples the estimation of position and color and balances the learning of low-level pixel intensity and high-level semantics details. The framework estimates the position offset via a Position Net and the color information via a Texture Net. By decoupling, the network will neither zero the opacity nor learn the background color at an inaccurate initial position, and can learn the exact 3D Gaussian position on a preset number of Gaussians. Besides, we introduce an adaptive thresholding strategy that dynamically shrinks the LPIPS loss during training. This strategy balances the learning of low-level pixel intensity and high-level semantics. Experimental results on public datasets show that our method achieves better quality of appearance and shorter training time.