Joint Gaussian Deformation in Triangle-Deformed Space for High-Fidelity Head Avatars
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Creating 3D human heads with mesoscale details and high-fidelity animation from monocular or sparse multi-view videos is challenging. While 3D Gaussian splatting (3DGS) has brought significant benefits into this task, due to its powerful representation ability and rendering speed, existing works still face several issues, including inaccurate and blurry deformation, and lack of detailed appearance, due to difficulties in complex deformation representation and unreasonable Gaussian placement. In this paper, we propose a joint Gaussian deformation method by decoupling the complex deformation into two simpler deformations, incorporating a learnable displacement map-guided Gaussian-triangle binding and a neural-based deformation refinement, improving the fidelity of animation and details of reconstructed head avatars. However, renderings of reconstructed head avatars at unseen views still show artifacts, due to overfitting on sparse input views. To address this issue, we leverage synthesized pseudo views rendered with fitted textured 3DMMs as priors to initialize Gaussians, which helps maintain a consistent and realistic appearance across various views. As a result, our method outperforms existing state-of-the-art approaches with about 4.3 dB PSNR in novel-view synthesis and about 0.9 dB PSNR in self-reenactment on multi-view video datasets. Our method also preserves high-frequency details, exhibits more accurate deformations, and significantly reduces artifacts in unseen views.
Description
CCS Concepts: Computing methodologies -> Rendering
@inproceedings{10.2312:sr.20251189,
booktitle = {Eurographics Symposium on Rendering},
editor = {Wang, Beibei and Wilkie, Alexander},
title = {{Joint Gaussian Deformation in Triangle-Deformed Space for High-Fidelity Head Avatars}},
author = {Lu, Jiawei and Guang, Kunxin and Hao, Conghui and Sun, Kai and Yang, Jian and Xie, Jin and Wang, Beibei},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1727-3463},
ISBN = {978-3-03868-292-9},
DOI = {10.2312/sr.20251189}
}