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

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arxiv 2404.07991 v1 pith:NVKRSBAT submitted 2024-04-11 cs.CV

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

Jing Wen , Xiaoming Zhao , Zhongzheng Ren , Alexander G. Schwing , Shenlong Wang This is my paper
classification cs.CV
keywords gomavatarmodelinghumanmonocularrenderinganimatablegaussians-on-meshmemory-efficient
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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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    A two-stage Gaussian-splatting framework with supervisor feature regularization and online animation augmentation improves monocular textured 3D human reconstruction on CustomHuman and THuman3.0.