  In this paper, we present a novel method that facilitates the creation of
vivid 3D Gaussian avatars from monocular video inputs (GVA). Our innovation
lies in addressing the intricate challenges of delivering high-fidelity human
body reconstructions and aligning 3D Gaussians with human skin surfaces
accurately. The key contributions of this paper are twofold. Firstly, we
introduce a pose refinement technique to improve hand and foot pose accuracy by
aligning normal maps and silhouettes. Precise pose is crucial for correct shape
and appearance reconstruction. Secondly, we address the problems of unbalanced
aggregation and initialization bias that previously diminished the quality of
3D Gaussian avatars, through a novel surface-guided re-initialization method
that ensures accurate alignment of 3D Gaussian points with avatar surfaces.
Experimental results demonstrate that our proposed method achieves
high-fidelity and vivid 3D Gaussian avatar reconstruction. Extensive
experimental analyses validate the performance qualitatively and
quantitatively, demonstrating that it achieves state-of-the-art performance in
photo-realistic novel view synthesis while offering fine-grained control over
the human body and hand pose. Project page: https://3d-aigc.github.io/GVA/.
