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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.25183 |
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| _version_ | 1866912615282769920 |
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| author | Liao, Ting-Hsuan Liu, Haowen Xu, Yiran Ge, Songwei Yang, Gengshan Huang, Jia-Bin |
| author_facet | Liao, Ting-Hsuan Liu, Haowen Xu, Yiran Ge, Songwei Yang, Gengshan Huang, Jia-Bin |
| contents | We present PAD3R, a method for reconstructing deformable 3D objects from casually captured, unposed monocular videos. Unlike existing approaches, PAD3R handles long video sequences featuring substantial object deformation, large-scale camera movement, and limited view coverage that typically challenge conventional systems. At its core, our approach trains a personalized, object-centric pose estimator, supervised by a pre-trained image-to-3D model. This guides the optimization of deformable 3D Gaussian representation. The optimization is further regularized by long-term 2D point tracking over the entire input video. By combining generative priors and differentiable rendering, PAD3R reconstructs high-fidelity, articulated 3D representations of objects in a category-agnostic way. Extensive qualitative and quantitative results show that PAD3R is robust and generalizes well across challenging scenarios, highlighting its potential for dynamic scene understanding and 3D content creation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos Liao, Ting-Hsuan Liu, Haowen Xu, Yiran Ge, Songwei Yang, Gengshan Huang, Jia-Bin Computer Vision and Pattern Recognition We present PAD3R, a method for reconstructing deformable 3D objects from casually captured, unposed monocular videos. Unlike existing approaches, PAD3R handles long video sequences featuring substantial object deformation, large-scale camera movement, and limited view coverage that typically challenge conventional systems. At its core, our approach trains a personalized, object-centric pose estimator, supervised by a pre-trained image-to-3D model. This guides the optimization of deformable 3D Gaussian representation. The optimization is further regularized by long-term 2D point tracking over the entire input video. By combining generative priors and differentiable rendering, PAD3R reconstructs high-fidelity, articulated 3D representations of objects in a category-agnostic way. Extensive qualitative and quantitative results show that PAD3R is robust and generalizes well across challenging scenarios, highlighting its potential for dynamic scene understanding and 3D content creation. |
| title | PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.25183 |