FAGhead: Fully Animate Gaussian Head from Monocular Videos
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913406984912896 |
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| author | Xuan, Yixin Li, Xinyang Yao, Gongxin Zhou, Shiwei Sun, Donghui Chen, Xiaoxin Pan, Yu |
| author_facet | Xuan, Yixin Li, Xinyang Yao, Gongxin Zhou, Shiwei Sun, Donghui Chen, Xiaoxin Pan, Yu |
| contents | High-fidelity reconstruction of 3D human avatars has a wild application in visual reality. In this paper, we introduce FAGhead, a method that enables fully controllable human portraits from monocular videos. We explicit the traditional 3D morphable meshes (3DMM) and optimize the neutral 3D Gaussians to reconstruct with complex expressions. Furthermore, we employ a novel Point-based Learnable Representation Field (PLRF) with learnable Gaussian point positions to enhance reconstruction performance. Meanwhile, to effectively manage the edges of avatars, we introduced the alpha rendering to supervise the alpha value of each pixel. Extensive experimental results on the open-source datasets and our capturing datasets demonstrate that our approach is able to generate high-fidelity 3D head avatars and fully control the expression and pose of the virtual avatars, which is outperforming than existing works. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19070 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FAGhead: Fully Animate Gaussian Head from Monocular Videos Xuan, Yixin Li, Xinyang Yao, Gongxin Zhou, Shiwei Sun, Donghui Chen, Xiaoxin Pan, Yu Computer Vision and Pattern Recognition High-fidelity reconstruction of 3D human avatars has a wild application in visual reality. In this paper, we introduce FAGhead, a method that enables fully controllable human portraits from monocular videos. We explicit the traditional 3D morphable meshes (3DMM) and optimize the neutral 3D Gaussians to reconstruct with complex expressions. Furthermore, we employ a novel Point-based Learnable Representation Field (PLRF) with learnable Gaussian point positions to enhance reconstruction performance. Meanwhile, to effectively manage the edges of avatars, we introduced the alpha rendering to supervise the alpha value of each pixel. Extensive experimental results on the open-source datasets and our capturing datasets demonstrate that our approach is able to generate high-fidelity 3D head avatars and fully control the expression and pose of the virtual avatars, which is outperforming than existing works. |
| title | FAGhead: Fully Animate Gaussian Head from Monocular Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.19070 |