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.
以往的头像头部虚拟化方法主要依赖于固定形状的场景基元，缺乏几何拓扑、纹理细节和计算效率之间的平衡。一些混合神经网络方法（例如，平面和体素）在快速渲染方面获得了优势，但它们都使用轴对齐映射来显式提取特征，导致了轴对齐偏差和特征稀释的问题。我们提出了GaussianHead，它利用可变形的3D高斯作为头像头部的构建块。我们提出了一种新颖的方法，其中用于渲染的核心高斯在映射到因子平面以获取规范子因子之前会经历动态扩散。通过我们的因子混合策略，用于渲染中的核心高斯的规范特征得以获得。这种方法偏离了以前使用轴对齐映射的做法，尤其是在提升细微结构（如牙齿、皱纹、头发甚至面部毛孔）的表示能力方面。与最先进的方法相比，我们的GaussianHead独特的原始选择和因子分解提供了卓越的视觉结果，同时保持了渲染性能（每帧0.1秒）。