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.

高保真度 3D 头像模型的研究中，轻量级稀疏视图下依然存在很大挑战。本文提出了用可控的 3D 高斯模型表示的高保真度的头像模型。我们优化了中性 3D 高斯模型以及基于完全学习的 MLP 变形场，用于捕捉复杂表情。这两部分互相有益，因此我们的方法可以在保证表情精度的同时模拟细致的动态细节。此外，我们基于隐式 SDF 和深度 Marching Tetrahedra 设计了一个良好的几何引导初始化策略，以确保训练过程的稳定性和收敛性。实验证明，我们的方法优于其他最先进的稀疏视图方法，在夸张的表情下实现了 2K 分辨率下的超高保真度渲染质量。