Abstract
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.
Abstract (translated)
现有的3D头像重建方法通常采用两阶段过程，首先基于面部特征点追踪FLAME网格（参数化人脸模型），然后通过高斯渲染进行图像生成。然而，由于估计的网格和目标图像之间的对齐误差，这种传统的方法往往会导致渲染质量不佳，并且会丢失精细的视觉细节。 在本文中，我们提出了MoGaFace，这是一种新颖的3D头像建模框架，它在整个高斯渲染过程中持续细化面部几何形状和纹理属性。为了应对估计出的FLAME网格与目标图像之间的对齐问题，我们引入了“动量引导的一致几何”模块，该模块结合了一个基于动量更新的表情库以及一个表情感知修正机制，以确保时间上的连贯性和多视角一致性。 此外，我们还提出了潜在纹理注意力机制，它将紧凑的多视图特征编码为头部感知表示，并通过将其融入高斯函数中来进行几何感知的纹理细化。广泛的实验表明，MoGaFace能够在不准确的网格初始化以及不受限制的真实世界设置下实现高质量、高保真的头像重建和新颖视角合成能力。
URL
https://arxiv.org/abs/2508.01218
