用少量随意拍摄照片快速生成可换发型的高保真3D人脸模型。
FHAvatar: Fast and High-Fidelity Reconstruction of Face-and-Hair Composable 3D Head Avatar from Few Casual Captures
- 分开展现面部与头发的高斯表示，实现组件化建模。
- 仅需少数视角图像即可在数分钟内完成高质量重建。
- 支持实时动画与发型替换，适合内容创作者使用。
我们提出FHAvatar，一种从任意数量视角图像中重建可组合面部与头发的3D高斯头像的新框架。与以往将面部与头发统一建模的方法不同，我们在纹理空间显式解耦二者：面部采用平面高斯表示，头发采用基于线段的高斯表示。为克服现有方法依赖密集多视角采集或昂贵的个体优化的问题，我们设计了聚合变压器主干网络，从多视角数据集中学习几何感知的跨视角先验与头身结构一致性，从而高效提取并融合少样本随意拍摄图像中的特征。大量定量与定性实验表明，FHAvatar仅需少量新身份观测即可在几分钟内实现顶尖重建质量，同时支持实时动画、便捷发型迁移和风格化编辑，显著提升数字头像创作的可及性与应用范围。
原文摘要 · Abstract (English)
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.
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