MGAvatar：用于头部虚拟化身几何与外观建模的网格约束高斯
MGAvatar: Mesh-Bound Gaussians for Head Avatar Geometry and Appearance Modeling
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- Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）
- Xiamen University（厦门大学）
- The University of Texas at Dallas（德克萨斯大学达拉斯分校）
- Jimei University（集美大学）
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中文总结 AI 辅助
MGAvatar提出高斯-网格混合表示，通过顶点绑定和面绑定两种模式联合建模头部几何与外观，引入视角条件神经颜色场和高斯偏移网络，提升渲染质量，生成高保真头部虚拟化身。
中文摘要 AI 辅助
精确的头部建模需要一种稳定且富有表现力的几何表示。现有的基于高斯的头部虚拟化身通常依赖参数化模板（如FLAME）进行高斯初始化和变形，但这些模板缺乏个性化先验，难以表示头发和衣物等结构。为解决此问题，我们提出MGAvatar，一种高斯-网格混合表示，通过两种高斯-网格绑定模式联合建模几何和外观。具体而言，我们引入顶点绑定高斯并约束其可学习参数，实现渐进式网格变形以表示复杂的头部几何，同时一个姿态相关偏移模块处理非刚性变形。一旦几何稳定，MGAvatar切换到面绑定高斯进行外观建模。为提高跨新姿态和新视角的外观一致性，我们引入一个视角条件神经颜色场，以减轻独立优化高斯颜色引起的伪影。此外，我们设计一个高斯偏移网络，在观测空间中预测高斯偏移图，为面绑定高斯提供更大灵活性以捕捉动态面部纹理。在多视角和单目视频上的大量实验表明，MGAvatar在渲染质量上优于现有方法，生成具有丰富纹理细节的高保真头部虚拟化身。
英文摘要
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.
