Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908390721060864 |
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| author | Sang, Chen Qian, Yeqiang Zhang, Jiale Wang, Chunxiang Yang, Ming |
| author_facet | Sang, Chen Qian, Yeqiang Zhang, Jiale Wang, Chunxiang Yang, Ming |
| contents | For tasks such as urban digital twins, VR/AR/game scene design, or creating synthetic films, the traditional industrial approach often involves manually modeling scenes and using various rendering engines to complete the rendering process. This approach typically requires high labor costs and hardware demands, and can result in poor quality when replicating complex real-world scenes. A more efficient approach is to use data from captured real-world scenes, then apply reconstruction and rendering algorithms to quickly recreate the authentic scene. However, current algorithms are unable to effectively reconstruct and render real-world weather effects. To address this, we propose a framework based on gaussian splatting, that can reconstruct real scenes and render them under synthesized 4D weather effects. Our work can simulate various common weather effects by applying Gaussians modeling and rendering techniques. It supports continuous dynamic weather changes and can easily control the details of the effects. Additionally, our work has low hardware requirements and achieves real-time rendering performance. The result demos can be accessed on our project homepage: weathermagician.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time Sang, Chen Qian, Yeqiang Zhang, Jiale Wang, Chunxiang Yang, Ming Computer Vision and Pattern Recognition For tasks such as urban digital twins, VR/AR/game scene design, or creating synthetic films, the traditional industrial approach often involves manually modeling scenes and using various rendering engines to complete the rendering process. This approach typically requires high labor costs and hardware demands, and can result in poor quality when replicating complex real-world scenes. A more efficient approach is to use data from captured real-world scenes, then apply reconstruction and rendering algorithms to quickly recreate the authentic scene. However, current algorithms are unable to effectively reconstruct and render real-world weather effects. To address this, we propose a framework based on gaussian splatting, that can reconstruct real scenes and render them under synthesized 4D weather effects. Our work can simulate various common weather effects by applying Gaussians modeling and rendering techniques. It supports continuous dynamic weather changes and can easily control the details of the effects. Additionally, our work has low hardware requirements and achieves real-time rendering performance. The result demos can be accessed on our project homepage: weathermagician.github.io |
| title | Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.19919 |