Previous head avatar methods have primarily relied on fixed-shape scene primitives, lacking a balance between geometric topology, texture details, and computational efficiency. Some hybrid neural network methods (e.g., planes and voxels) gained advantages in fast rendering, but they all used axis-aligned mappings to extract features explicitly, leading to issues of axis-aligned bias and feature dilution. We present GaussianHead, which utilizes deformable 3D Gaussians as building blocks for the head avatars. We propose a novel methodology where the core Gaussians designated for rendering undergo dynamic diffusion before being mapped onto a factor plane to acquire canonical sub-factors. Through our factor blending strategy, the canonical features for the core Gaussians used in rendering are obtained. This approach deviates from the previous practice of utilizing axis-aligned mappings, especially improving the representation capability of subtle structures such as teeth, wrinkles, hair, and even facial pores. In comparison to state-of-the-art methods, our unique primitive selection and factor decomposition in GaussianHead deliver superior visual results while maintaining rendering performance (0.1 seconds per frame). Code will released for research.

翻译：以往的头部虚拟化身方法主要依赖固定形状的场景基元，在几何拓扑、纹理细节与计算效率之间缺乏平衡。部分混合神经网络方法（如平面与体素）在快速渲染方面具有优势，但均采用轴向对齐映射进行显式特征提取，导致轴向对齐偏差与特征稀释问题。我们提出GaussianHead，利用可变形三维高斯作为头部虚拟化身的构建模块。我们提出一种新颖的方法：指定用于渲染的核心高斯在映射到因子平面获取规范子因子之前，先进行动态扩散。通过因子融合策略，我们获得渲染所用核心高斯的规范特征。该方法摒弃了以往使用轴向对齐映射的做法，尤其增强了对牙齿、皱纹、头发甚至面部毛孔等细微结构的表征能力。与当前最优方法相比，GaussianHead独特的基元选择与因子分解策略在保持渲染性能（每帧0.1秒）的同时，提供了更优越的视觉结果。代码将在研究中开源。