GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh

• URL: http://arxiv.org/abs/2404.07991v1
• Date: Thu, 11 Apr 2024 17:59:57 GMT
• Title: GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
• Authors: Jing Wen, Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing, Shenlong Wang,
• Abstract summary: GoMAvatar is a novel approach for real-time, memory-efficient, high-quality human modeling. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality.
• Score: 97.47701169876272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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).

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