FHAvatar: Fast and High-Fidelity Reconstruction of Face-and-Hair Composable 3D Head Avatar from Few Casual Captures
Yujie Sun, Zhuoqiang Cai, Chaoyue Niu, Jianchuan Chen, Zhiwen Chen, Chengfei Lv, Fan Wu

TL;DR
FHAvatar is a fast, high-fidelity framework for reconstructing 3D face-and-hair avatars from minimal casual captures, enabling real-time animation and editing with state-of-the-art quality.
Contribution
It introduces a novel decoupled face and hair representation with an aggregated transformer backbone for efficient multi-view learning from few captures.
Findings
Achieves state-of-the-art reconstruction quality from limited views.
Supports real-time avatar animation and editing.
Reconstructs avatars within minutes from casual captures.
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…
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Taxonomy
TopicsFace recognition and analysis · Generative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis
