通过动态修正几何与纹理，实现高保真人脸3D建模。
MoGaFace: Momentum-Guided and Texture-Aware Gaussian Avatars for Consistent Facial Geometry
- 用动量更新表情库和感知校正机制，保持多视角一致性。
- 引入隐空间纹理注意力，提升渲染细节还原度。
- 适合需要真实感人脸重建的虚拟人、影视特效应用。
现有3D人脸动画重建方法采用两阶段流程：先基于面部关键点追踪得到FLAME网格，再进行基于高斯的渲染。然而，估计网格与目标图像间常存在错位，导致渲染质量下降且丢失细微视觉特征。本文提出MoGaFace，一种新型3D人脸建模框架，在高斯渲染过程中持续优化面部几何与纹理属性。为解决网格与图像间的错位问题，我们设计了动量引导的一致几何模块，结合动量更新的表情库与表达感知校正机制，保障时序与多视角一致性。同时提出潜在纹理注意力机制，将紧凑的多视角特征编码为头像感知表示，通过融入高斯分布实现几何感知的纹理精修。大量实验表明，即使在网格初始化不准确或真实无约束场景下，MoGaFace仍能实现高保真人脸建模，并显著提升新视角合成质量。
原文摘要 · Abstract (English)
Existing 3D head avatar reconstruction methods adopt a two-stage process, relying on tracked FLAME meshes derived from facial landmarks, followed by Gaussian-based rendering. However, misalignment between the estimated mesh and target images often leads to suboptimal rendering quality and loss of fine visual details. In this paper, we present MoGaFace, a novel 3D head avatar modeling framework that continuously refines facial geometry and texture attributes throughout the Gaussian rendering process. To address the misalignment between estimated FLAME meshes and target images, we introduce the Momentum-Guided Consistent Geometry module, which incorporates a momentum-updated expression bank and an expression-aware correction mechanism to ensure temporal and multi-view consistency. Additionally, we propose Latent Texture Attention, which encodes compact multi-view features into head-aware representations, enabling geometry-aware texture refinement via integration into Gaussians. Extensive experiments show that MoGaFace achieves high-fidelity head avatar reconstruction and significantly improves novel-view synthesis quality, even under inaccurate mesh initialization and unconstrained real-world settings.
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