Abstract
We present FHAvatar, a novel framework for reconstructing 3D Gaussian avatars with composable face and hair components from an arbitrary number of views. Unlike previous approaches that couple facial and hair representations within a unified modeling process, we explicitly decouple two components in texture space by representing the face with planar Gaussians and the hair with strand-based Gaussians. To overcome the limitations of existing methods that rely on dense multi-view captures or costly per-identity optimization, we propose an aggregated transformer backbone to learn geometry-aware cross-view priors and head-hair structural coherence from multi-view datasets, enabling effective and efficient feature extraction and fusion from few casual captures. Extensive quantitative and qualitative experiments demonstrate that FHAvatar achieves state-of-the-art reconstruction quality from only a few observations of new identities within minutes, while supporting real-time animation, convenient hairstyle transfer, and stylized editing, broadening the accessibility and applicability of digital avatar creation.
Abstract (translated)
我们提出了FHAvatar，一种新颖的框架，可从任意数量的视角中重建具有可组合面部与发丝组件的三维高斯虚拟形象。与以往在统一建模过程中耦合面部与发丝表征的方法不同，我们通过在纹理空间中用平面高斯表示面部、用基于发丝的高斯表示发丝，显式解耦这两个组件。为克服现有方法依赖密集多视角采集或昂贵逐身份优化的局限，我们提出聚合Transformer主干网络，从多视角数据集中学习几何感知的跨视角先验与头-发结构一致性，从而实现对少量 casually captured 数据的高效特征提取与融合。大量定量与定性实验表明，FHAvatar在数分钟内即可从新身份的少量观测中实现最先进的重建质量，同时支持实时动画、便捷发型迁移与风格化编辑，拓宽了数字虚拟形象创建的普及性与应用范围。
URL
https://arxiv.org/abs/2603.23345
