GaussianHead: Impressive Head Avatars with Learnable Gaussian Diffusion

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.

@article{wang2025_2312.01632,
  title={ GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation },
  author={ Jie Wang and Jiu-Cheng Xie and Xianyan Li and Feng Xu and Chi-Man Pun and Hao Gao },
  journal={arXiv preprint arXiv:2312.01632},
  year={ 2025 }
}