GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866918534057033728 |
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| author | Huang, Jiajun Xu, Shuolin Yu, Hongchuan Lee, Tong-Yee |
| author_facet | Huang, Jiajun Xu, Shuolin Yu, Hongchuan Lee, Tong-Yee |
| contents | We present GSDeformer, a method that enables cage-based deformation on 3D Gaussian Splatting (3DGS). Our approach bridges cage-based deformation and 3DGS by using a proxy point-cloud representation. This point cloud is generated from 3D Gaussians, and deformations applied to the point cloud are translated into transformations on the 3D Gaussians. To handle potential bending caused by deformation, we incorporate a splitting process to approximate it. Our method does not modify or extend the core architecture of 3D Gaussian Splatting, making it compatible with any trained vanilla 3DGS or its variants. Additionally, we automate cage construction for 3DGS and its variants using a render-and-reconstruct approach. Experiments demonstrate that GSDeformer delivers superior deformation results compared to existing methods, is robust under extreme deformations, requires no retraining for editing, runs in real-time, and can be extended to other 3DGS variants. Project Page: https://jhuangbu.github.io/gsdeformer/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_15491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting Huang, Jiajun Xu, Shuolin Yu, Hongchuan Lee, Tong-Yee Computer Vision and Pattern Recognition We present GSDeformer, a method that enables cage-based deformation on 3D Gaussian Splatting (3DGS). Our approach bridges cage-based deformation and 3DGS by using a proxy point-cloud representation. This point cloud is generated from 3D Gaussians, and deformations applied to the point cloud are translated into transformations on the 3D Gaussians. To handle potential bending caused by deformation, we incorporate a splitting process to approximate it. Our method does not modify or extend the core architecture of 3D Gaussian Splatting, making it compatible with any trained vanilla 3DGS or its variants. Additionally, we automate cage construction for 3DGS and its variants using a render-and-reconstruct approach. Experiments demonstrate that GSDeformer delivers superior deformation results compared to existing methods, is robust under extreme deformations, requires no retraining for editing, runs in real-time, and can be extended to other 3DGS variants. Project Page: https://jhuangbu.github.io/gsdeformer/ |
| title | GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.15491 |