We propose GGAvatar, a novel 3D avatar representation designed to robustly model dynamic head avatars with complex identities and deformations. GGAvatar employs a coarse-to-fine structure, featuring two core modules: Neutral Gaussian Initialization Module and Geometry Morph Adjuster. Neutral Gaussian Initialization Module pairs Gaussian primitives with deformable triangular meshes, employing an adaptive density control strategy to model the geometric structure of the target subject with neutral expressions. Geometry Morph Adjuster introduces deformation bases for each Gaussian in global space, creating fine-grained low-dimensional representations of deformation behaviors to address the Linear Blend Skinning formula's limitations effectively. Extensive experiments show that GGAvatar can produce high-fidelity renderings, outperforming state-of-the-art methods in visual quality and quantitative metrics.

翻译：我们提出GGAvatar，一种新型三维虚拟形象表示方法，旨在鲁棒地建模具有复杂身份特征和形变的动态头部模型。GGAvatar采用由粗到精的结构，包含两个核心模块：中性高斯初始化模块与几何形态调整器。中性高斯初始化模块将高斯基元与可变形三角网格配对，通过自适应密度控制策略建模具有中性表情的目标对象几何结构。几何形态调整器为全局空间中的每个高斯引入形变基，构建形变行为的细粒度低维表示，有效克服线性混合蒙皮公式的局限性。大量实验表明，GGAvatar能够生成高保真渲染结果，在视觉质量和量化指标上均超越现有最优方法。