  Creating high-fidelity 3D head avatars has always been a research hotspot,
but there remains a great challenge under lightweight sparse view setups. In
this paper, we propose Gaussian Head Avatar represented by controllable 3D
Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D
Gaussians and a fully learned MLP-based deformation field to capture complex
expressions. The two parts benefit each other, thereby our method can model
fine-grained dynamic details while ensuring expression accuracy. Furthermore,
we devise a well-designed geometry-guided initialization strategy based on
implicit SDF and Deep Marching Tetrahedra for the stability and convergence of
the training procedure. Experiments show our approach outperforms other
state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering
quality at 2K resolution even under exaggerated expressions.
