分离头与头发建模，实现更逼真的3D人脸动画
LUCAS: Layered Universal Codec Avatars
- 分层结构分别建模无发头和头发，解耦动态交互
- 在零样本驱动下仍保持高保真，支持多种表情和发型变化
- 基于网格的统一模型，适合移动端实时渲染
真实感3D人脸头像重建面临动态面部-头发交互建模和跨身份泛化难题，尤其在表情和头部运动时。我们提出LUCAS，一种新型通用先验模型（UPM）用于编码器-解码器头像建模，通过分层表示解耦面部与头发。不同于以往将头发视为头部整体的UPM，LUCAS将无发头与头发建模分离为独立分支。LUCAS是首个基于网格的UPM，支持设备端实时渲染。其分层结构还优化了锚定几何，提升高斯渲染的精度与视觉效果。实验表明，LUCAS在定量与定性评估中均优于现有单网格及基于高斯的头像模型，包括对未见受试者的零样本驱动测试。其在头部姿态变化、表情迁移和发型差异处理上表现更优，推动了3D头像重建的最新进展。
原文摘要 · Abstract (English)
Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during expressions and head movements. We present LUCAS, a novel Universal Prior Model (UPM) for codec avatar modeling that disentangles face and hair through a layered representation. Unlike previous UPMs that treat hair as an integral part of the head, our approach separates the modeling of the hairless head and hair into distinct branches. LUCAS is the first to introduce a mesh-based UPM, facilitating real-time rendering on devices. Our layered representation also improves the anchor geometry for precise and visually appealing Gaussian renderings. Experimental results indicate that LUCAS outperforms existing single-mesh and Gaussian-based avatar models in both quantitative and qualitative assessments, including evaluations on held-out subjects in zero-shot driving scenarios. LUCAS demonstrates superior dynamic performance in managing head pose changes, expression transfer, and hairstyle variations, thereby advancing the state-of-the-art in 3D head avatar reconstruction.
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