Learning Explicit Continuous Motion Representation for Dynamic Gaussian Splatting from Monocular Videos
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914423016259584 |
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| author | Zhang, Xuankai Xiao, Junjin Huang, Shangwei Zheng, Wei-shi Zhang, Qing |
| author_facet | Zhang, Xuankai Xiao, Junjin Huang, Shangwei Zheng, Wei-shi Zhang, Qing |
| contents | We present an approach for high-quality dynamic Gaussian Splatting from monocular videos. To this end, we in this work go one step further beyond previous methods to explicitly model continuous position and orientation deformation of dynamic Gaussians, using an SE(3) B-spline motion bases with a compact set of control points. To improve computational efficiency while enhancing the ability to model complex motions, an adaptive control mechanism is devised to dynamically adjust the number of motion bases and control points. Besides, we develop a soft segment reconstruction strategy to mitigate long-interval motion interference, and employ a multi-view diffusion model to provide multi-view cues for avoiding overfitting to training views. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in novel view synthesis. Our code is available at https://github.com/hhhddddddd/se3bsplinegs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25058 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Explicit Continuous Motion Representation for Dynamic Gaussian Splatting from Monocular Videos Zhang, Xuankai Xiao, Junjin Huang, Shangwei Zheng, Wei-shi Zhang, Qing Computer Vision and Pattern Recognition We present an approach for high-quality dynamic Gaussian Splatting from monocular videos. To this end, we in this work go one step further beyond previous methods to explicitly model continuous position and orientation deformation of dynamic Gaussians, using an SE(3) B-spline motion bases with a compact set of control points. To improve computational efficiency while enhancing the ability to model complex motions, an adaptive control mechanism is devised to dynamically adjust the number of motion bases and control points. Besides, we develop a soft segment reconstruction strategy to mitigate long-interval motion interference, and employ a multi-view diffusion model to provide multi-view cues for avoiding overfitting to training views. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in novel view synthesis. Our code is available at https://github.com/hhhddddddd/se3bsplinegs. |
| title | Learning Explicit Continuous Motion Representation for Dynamic Gaussian Splatting from Monocular Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.25058 |