HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors
Authors: name: Xiaozheng Zheng, name: Chao Wen, name: Zhaohu Li, name: Weiyi Zhang, name: Zhuo Su, name: Xu Chang, name: Yang Zhao, name: Zheng Lv, name: Xiaoyuan Zhang, name: Yongjie Zhang, name: Guidong Wang, name: Lan Xu

In this paper, we present a novel 3D head avatar creation approach capable of
generalizing from few-shot in-the-wild data with high-fidelity and animatable
robustness. Given the underconstrained nature of this problem, incorporating
prior knowledge is essential. Therefore, we propose a framework comprising
prior learning and avatar creation phases. The prior learning phase leverages
3D head priors derived from a large-scale multi-view dynamic dataset, and the
avatar creation phase applies these priors for few-shot personalization. Our
approach effectively captures these priors by utilizing a Gaussian
Splatting-based auto-decoder network with part-based dynamic modeling. Our
method employs identity-shared encoding with personalized latent codes for
individual identities to learn the attributes of Gaussian primitives. During
the avatar creation phase, we achieve fast head avatar personalization by
leveraging inversion and fine-tuning strategies. Extensive experiments
demonstrate that our model effectively exploits head priors and successfully
generalizes them to few-shot personalization, achieving photo-realistic
rendering quality, multi-view consistency, and stable animation.