GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
Abstract
We introduce GoMAvatar a novel approach for real-time memory-efficient high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel viewpoints while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh (GoM) representation a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap PeopleSnapshot and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).
Cite
Text
Wen et al. "GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00201Markdown
[Wen et al. "GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/wen2024cvpr-gomavatar/) doi:10.1109/CVPR52733.2024.00201BibTeX
@inproceedings{wen2024cvpr-gomavatar,
title = {{GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh}},
author = {Wen, Jing and Zhao, Xiaoming and Ren, Zhongzheng and Schwing, Alexander G. and Wang, Shenlong},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2024},
pages = {2059-2069},
doi = {10.1109/CVPR52733.2024.00201},
url = {https://mlanthology.org/cvpr/2024/wen2024cvpr-gomavatar/}
}