GaussianHead: Impressive 3D Gaussian-based Head Avatars with Dynamic Hybrid Neural Field

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.

@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 }
}