Given the significant potential of 3D Gaussian Splatting for high-fidelity avatar reconstruction, we propose an end-to-end framework that reconstructs a high-quality 3D Gaussian head from a single face image without any 2D post-processing. Given a near-frontal face image, the network directly predicts a 2D UV-aligned Gaussian attribute map consistent with the FLAME template, enabling efficient sampling of Gaussian primitives and fast rendering from arbitrary viewpoints, including unseen rear views. To balance global identity information and fine details, we adopt a dual-branch design: a pretrained VQ-VAE–based face prior branch encodes identity and coarse geometry via a codebook. In contrast, a ViT-based branch extracts high-frequency texture details from the input image. These branches fuse in UV space to produce precise Gaussian attributes for position, scale, color, and opacity. This dual-branch setup maximally captures face-relevant information from the input. We train on a large synthetic multi-view dataset, using cross-view loss formulation and Gaussian attribute regularization to supervise rendered images. Experiments demonstrate that our method outperforms existing single-image reconstruction approaches on various metrics and excels in generalization, identity preservation, and multi-view consistency.