GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916201709436928 |
|---|---|
| author | Wen, Jing Zhao, Xiaoming Ren, Zhongzheng Schwing, Alexander G. Wang, Shenlong |
| author_facet | Wen, Jing Zhao, Xiaoming Ren, Zhongzheng Schwing, Alexander G. Wang, Shenlong |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07991 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh Wen, Jing Zhao, Xiaoming Ren, Zhongzheng Schwing, Alexander G. Wang, Shenlong Computer Vision and Pattern Recognition 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). |
| title | GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.07991 |