GGAvatar: Geometric Adjustment of Gaussian Head Avatar

Autor:	Li, Xinyang, Wang, Jiaxin, Xuan, Yixin, Yao, Gongxin, Pan, Yu
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.11993 Zobrazit plný text záznamu View this record from Arxiv