Global Motion Corresponder for 3D Point-Based Scene Interpolation under Large Motion
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916922696663040 |
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| author | Lin, Junru Vashist, Chirag Uy, Mikaela Angelina Stearns, Colton Luo, Xuan Guibas, Leonidas Li, Ke |
| author_facet | Lin, Junru Vashist, Chirag Uy, Mikaela Angelina Stearns, Colton Luo, Xuan Guibas, Leonidas Li, Ke |
| contents | Existing dynamic scene interpolation methods typically assume that the motion between consecutive timesteps is small enough so that displacements can be locally approximated by linear models. In practice, even slight deviations from this small-motion assumption can cause conventional techniques to fail. In this paper, we introduce Global Motion Corresponder (GMC), a novel approach that robustly handles large motion and achieves smooth transitions. GMC learns unary potential fields that predict SE(3) mappings into a shared canonical space, balancing correspondence, spatial and semantic smoothness, and local rigidity. We demonstrate that our method significantly outperforms existing baselines on 3D scene interpolation when the two states undergo large global motions. Furthermore, our method enables extrapolation capabilities where other baseline methods cannot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20136 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Global Motion Corresponder for 3D Point-Based Scene Interpolation under Large Motion Lin, Junru Vashist, Chirag Uy, Mikaela Angelina Stearns, Colton Luo, Xuan Guibas, Leonidas Li, Ke Image and Video Processing Existing dynamic scene interpolation methods typically assume that the motion between consecutive timesteps is small enough so that displacements can be locally approximated by linear models. In practice, even slight deviations from this small-motion assumption can cause conventional techniques to fail. In this paper, we introduce Global Motion Corresponder (GMC), a novel approach that robustly handles large motion and achieves smooth transitions. GMC learns unary potential fields that predict SE(3) mappings into a shared canonical space, balancing correspondence, spatial and semantic smoothness, and local rigidity. We demonstrate that our method significantly outperforms existing baselines on 3D scene interpolation when the two states undergo large global motions. Furthermore, our method enables extrapolation capabilities where other baseline methods cannot. |
| title | Global Motion Corresponder for 3D Point-Based Scene Interpolation under Large Motion |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2508.20136 |