Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians
Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, Yebin Liu
Abstract
Creating high-fidelity 3D head avatars has always been a research hotspot but there remains a great challenge under lightweight sparse view setups. In this paper we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions. Project page: https://yuelangx.github.io/gaussianheadavatar.
BibTeX
@inproceedings{cvpr2024_gaussianheadavat,
title = {Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians},
author = {Yuelang Xu and Benwang Chen and Zhe Li and Hongwen Zhang and Lizhen Wang and Zerong Zheng and Yebin Liu},
booktitle = {CVPR 2024},
year = {2024}
}