PAOLI: Pose-free Articulated Object Learning from Sparse-view Images
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914441219538944 |
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| author | Deng, Jianning Subr, Kartic Bilen, Hakan |
| author_facet | Deng, Jianning Subr, Kartic Bilen, Hakan |
| contents | We present a methodology to model articulated objects using a sparse set of images with unknown poses.
Current methods require dense multi-view observations and ground-truth camera poses. Our approach operates with as few as four views per articulation and no camera supervision. Our central insight is to first solve a robust correspondence and alignment problem between unaligned reconstructions, before part motions can be analyzed. We first reconstruct each articulation independently using recent advances in sparse-view 3D reconstruction, then learn a deformation field that establishes dense correspondences across poses. A progressive disentanglement strategy further separates static from moving parts, enabling robust separation of camera and object motion. Finally, we optimize geometry, appearance, and kinematics jointly with a self-supervised loss that enforces cross-view and cross-pose consistency. Experiments on the standard benchmark and real-world examples demonstrate that our method produces accurate and detailed articulated object representations under significantly weaker input assumptions than existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PAOLI: Pose-free Articulated Object Learning from Sparse-view Images Deng, Jianning Subr, Kartic Bilen, Hakan Computer Vision and Pattern Recognition We present a methodology to model articulated objects using a sparse set of images with unknown poses. Current methods require dense multi-view observations and ground-truth camera poses. Our approach operates with as few as four views per articulation and no camera supervision. Our central insight is to first solve a robust correspondence and alignment problem between unaligned reconstructions, before part motions can be analyzed. We first reconstruct each articulation independently using recent advances in sparse-view 3D reconstruction, then learn a deformation field that establishes dense correspondences across poses. A progressive disentanglement strategy further separates static from moving parts, enabling robust separation of camera and object motion. Finally, we optimize geometry, appearance, and kinematics jointly with a self-supervised loss that enforces cross-view and cross-pose consistency. Experiments on the standard benchmark and real-world examples demonstrate that our method produces accurate and detailed articulated object representations under significantly weaker input assumptions than existing approaches. |
| title | PAOLI: Pose-free Articulated Object Learning from Sparse-view Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.04276 |