MGAvatar: Mesh-Bound Gaussians for Head Avatar Geometry and Appearance Modeling

2026-09-11

AI Summary

MGAvatar通过结合高斯-网格混合表示方法解决现有头部模型缺乏个性化和复杂结构表达的问题，提高了头像几何和外观建模的准确性。

Abstract

Accurate head modeling requires a stable yet expressive geometric representation. Existing Gaussian-based head avatars commonly rely on parametric templates (e.g., FLAME) for Gaussian initialization and deformation, but these templates lack personalized priors and struggle to represent structures such as hair and clothing. To address this issue, we propose MGAvatar, a Gaussian-mesh hybrid representation that jointly models geometry and appearance through two Gaussian-mesh binding modes. Specifically, we introduce vertex-bound Gaussians and constrain their learnable parameters, enabling progressive mesh deformation to represent complex head geometry, while a pose-dependent offset module accounts for non-rigid deformations. Once geometry is stabilized, MGAvatar switches to face-bound Gaussians for appearance modeling. To improve appearance consistency across novel poses and viewpoints, we introduce a view-conditioned neural color field that alleviates artifacts caused by independently optimized Gaussian colors. In addition, we design a Gaussian offset network to predict Gaussian offset maps in the observation space, providing greater flexibility for face-bound Gaussians to capture dynamic facial textures. Extensive experiments on multi-view and monocular videos show that MGAvatar outperforms existing methods in rendering quality, producing high-fidelity head avatars with rich texture details.

Problem

• Gaussian-based head avatars
• parametric templates
• personalized priors
• hair and clothing

Innovation

• Gaussian-mesh hybrid representation
• vertex-bound Gaussians
• view-conditioned neural color field
• Gaussian offset network