GGAvatar: Geometric Adjustment of Gaussian Head Avatar
This work addresses the challenge of creating realistic and robust 3D head avatars for applications like virtual reality or animation, representing an incremental improvement over existing methods.
The paper tackles the problem of modeling dynamic head avatars with complex identities and deformations by proposing GGAvatar, a 3D avatar representation that uses a coarse-to-fine structure with modules for neutral expression modeling and deformation adjustment, resulting in high-fidelity renderings that outperform state-of-the-art methods in visual quality and quantitative metrics.
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.