GGAvatar: Geometric Adjustment of Gaussian Head Avatar
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913357012926464 |
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| author | Li, Xinyang Wang, Jiaxin Xuan, Yixin Yao, Gongxin Pan, Yu |
| author_facet | Li, Xinyang Wang, Jiaxin Xuan, Yixin Yao, Gongxin Pan, Yu |
| contents | We propose GGAvatar, a novel 3D avatar representation designed to robustly model dynamic head avatars with complex identities and deformations. GGAvatar employs a coarse-to-fine structure, featuring two core modules: Neutral Gaussian Initialization Module and Geometry Morph Adjuster. Neutral Gaussian Initialization Module pairs Gaussian primitives with deformable triangular meshes, employing an adaptive density control strategy to model the geometric structure of the target subject with neutral expressions. Geometry Morph Adjuster introduces deformation bases for each Gaussian in global space, creating fine-grained low-dimensional representations of deformation behaviors to address the Linear Blend Skinning formula's limitations effectively. Extensive experiments show that GGAvatar can produce high-fidelity renderings, outperforming state-of-the-art methods in visual quality and quantitative metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11993 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GGAvatar: Geometric Adjustment of Gaussian Head Avatar Li, Xinyang Wang, Jiaxin Xuan, Yixin Yao, Gongxin Pan, Yu Computer Vision and Pattern Recognition We propose GGAvatar, a novel 3D avatar representation designed to robustly model dynamic head avatars with complex identities and deformations. GGAvatar employs a coarse-to-fine structure, featuring two core modules: Neutral Gaussian Initialization Module and Geometry Morph Adjuster. Neutral Gaussian Initialization Module pairs Gaussian primitives with deformable triangular meshes, employing an adaptive density control strategy to model the geometric structure of the target subject with neutral expressions. Geometry Morph Adjuster introduces deformation bases for each Gaussian in global space, creating fine-grained low-dimensional representations of deformation behaviors to address the Linear Blend Skinning formula's limitations effectively. Extensive experiments show that GGAvatar can produce high-fidelity renderings, outperforming state-of-the-art methods in visual quality and quantitative metrics. |
| title | GGAvatar: Geometric Adjustment of Gaussian Head Avatar |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.11993 |