Previous head avatar methods have mostly relied on fixed explicit primitives
(mesh, point) or implicit surfaces (Sign Distance Function) and volumetric
neural radiance field, it challenging to strike a balance among high fidelity,
training speed, and resource consumption. The recent popularity of hybrid field
has brought novel representation, but is limited by relying on parameterization
factors obtained through fixed mappings. We propose GaussianHead: an head
avatar algorithm based on anisotropic 3D gaussian primitives. We leverage
canonical gaussians to represent dynamic scenes. Using explicit "dynamic"
tri-plane as an efficient container for parameterized head geometry, aligned
well with factors in the underlying geometry and tri-plane, we obtain aligned
canonical factors for the canonical gaussians. With a tiny MLP, factors are
decoded into opacity and spherical harmonic coefficients of 3D gaussian
primitives. Finally, we use efficient differentiable gaussian rasterizer for
rendering. Our approach benefits significantly from our novel representation
based on 3D gaussians, and the proper alignment transformation of underlying
geometry structures and factors in tri-plane eliminates biases introduced by
fixed mappings. Compared to state-of-the-art techniques, we achieve optimal
visual results in tasks such as self-reconstruction, novel view synthesis, and
cross-identity reenactment while maintaining high rendering efficiency (0.12s
per frame). Even the pores around the nose are clearly visible in some cases.
Code and additional video can be found on the project homepage.

基于 3D 高斯原理，我们提出了一个名为 GaussianHead 的头像算法，它在头部几何和三面平面的因素对齐变换有效消除了固定映射引入的偏见，使得在自重建、新视图合成和跨身份再现等任务中获得了最佳的可视效果，并保持高渲染效率（每帧 0.12 秒）。有关代码和其他视频可以在项目主页上找到。