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).
我们介绍了一种名为GoMAvatar的全新方法，用于实时、高效、高质量的可动画人类建模。GoMAvatar的输入是单个单眼视频，可以创建能够在新姿势下重新构造，并从新的视点实时渲染的数字化头像，同时无缝集成到基于光栅化的图形管线中。我们方法的核心是高斯网格表示，这是一种结合了高斯平滑渲染的质量与速度和可变形网格的几何建模及兼容性的混合3D模型。我们在ZJU-MoCap数据和多个YouTube视频上评估了GoMAvatar。GoMAvatar在渲染质量上匹配或超越了当前的单眼人体建模算法，并在计算效率上显著优于它们（43 FPS），同时具有内存效率高（每个对象3.63 MB）。