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.
在本文中，我们提出了一种新颖的3D头像创建方法，该方法能够从少量的野外数据中实现高保真度和可动画的鲁棒性。鉴于此问题的欠约束性，融入先验知识至关重要。因此，我们提出了一个包含先验学习和头像创建阶段的框架。在先验学习阶段，我们利用从大规模多视角动态数据集中获取的3D头像先验信息；在头像创建阶段，我们将这些先验应用于少样本个性化。我们的方法通过使用基于高斯点绘的自动解码网络和基于部分的动态建模来有效捕捉这些先验。我们的方法采用身份共享编码，并结合个性化潜在代码来学习高斯基元的属性。在头像创建阶段，我们通过反向优化和微调策略，实现了快速的头像个性化。大量实验表明，我们的模型能够有效利用头像先验信息，并成功将其泛化到少样本个性化中，达到了照片级逼真的渲染质量、多视角一致性和稳定的动画效果。