  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.
