GoMAvatar: Efficient Animatable Human Modeling from Monocular Video
  Using Gaussians-on-Mesh
Jing Wen Xiaoming Zhao Zhongzheng Ren Alexander G. Schwing Shenlong Wang
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).