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).

GoMAvatar 是一种实时、内存高效、高质量可动画人体建模方法，利用高斯分布在网格上进行表达，实现新姿势的重新建模和新视角的实时渲染，并无缝集成光栅化图形流水线，其渲染质量超过当下的单目人体建模算法，并在计算效率（43 FPS）和内存占用（每个主体 3.63MB）方面显著优于它们。