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.

翻译：本文提出了一种新颖的三维头部化身生成方法，该方法能够从少量真实世界数据中泛化，具备高保真度和可动画化的鲁棒性。鉴于该问题的欠约束性质，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与化身生成两阶段的框架。先验学习阶段利用从大规模多视角动态数据集导出的三维头部先验，化身生成阶段则将这些先验应用于少样本个性化。我们的方法通过采用基于高斯泼溅的自解码器网络并结合基于部件的动态建模，有效地捕获了这些先验。该方法采用身份共享编码与针对个体身份的个人化潜在码，以学习高斯基元的属性。在化身生成阶段，我们通过利用反转与微调策略，实现了快速的头部化身个性化。大量实验表明，我们的模型有效利用了头部先验，并成功将其泛化至少样本个性化任务，实现了照片级真实感的渲染质量、多视角一致性以及稳定的动画效果。