GGAvatar: Dynamic Facial Geometric Adjustment for Gaussian Head Avatar
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Reconstructing animatable 3D head avatars from target subject videos has long been a significant challenge and a hot topic in computer graphics. This paper proposes 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: a Neutral Gaussian Initialization Module and a Geometry Morph Adjuster. The Neutral Gaussian Initialization Module pairs Gaussian primitives with deformable triangular meshes, using an adaptive density control strategy to model the geometric structure of the target subject with neutral expressions. The Geometry Morph Adjuster introduces deformation bases for each Gaussian in global space, creating fine-grained low-dimensional representations of deformations to overcome the limitations of the Linear Blend Skinning formula. Extensive experiments show that GGAvatar can produce high-fidelity renderings, outperforming state-of-the-art methods in visual quality and quantitative metrics.
Description
CCS Concepts: Computing methodologies → Reconstruction; Animation; Shape modeling
@inproceedings{10.2312:pg.20241313,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{GGAvatar: Dynamic Facial Geometric Adjustment for Gaussian Head Avatar}},
author = {Li, Xinyang and Wang, Jiaxin and Xuan, Yixin and Yao, Gongxin and Pan, Yu},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241313}
}