GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
<|reference_start|>GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh: 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 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 data 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).<|reference_end|>
@article{wen2024gomavatar:,
    title={GoMAvatar: Efficient Animatable Human Modeling from Monocular Video
  Using Gaussians-on-Mesh},
    author={Jing Wen, Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing,
  Shenlong Wang},
    journal={arXiv preprint arXiv:2404.07991},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07991},
    primaryClass={cs.CV}
}