DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914890279550976 |
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| author | Li, Ruining Zheng, Chuanxia Rupprecht, Christian Vedaldi, Andrea |
| author_facet | Li, Ruining Zheng, Chuanxia Rupprecht, Christian Vedaldi, Andrea |
| contents | We introduce DragAPart, a method that, given an image and a set of drags as input, generates a new image of the same object that responds to the action of the drags. Differently from prior works that focused on repositioning objects, DragAPart predicts part-level interactions, such as opening and closing a drawer. We study this problem as a proxy for learning a generalist motion model, not restricted to a specific kinematic structure or object category. We start from a pre-trained image generator and fine-tune it on a new synthetic dataset, Drag-a-Move, which we introduce. Combined with a new encoding for the drags and dataset randomization, the model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15382 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DragAPart: Learning a Part-Level Motion Prior for Articulated Objects Li, Ruining Zheng, Chuanxia Rupprecht, Christian Vedaldi, Andrea Computer Vision and Pattern Recognition We introduce DragAPart, a method that, given an image and a set of drags as input, generates a new image of the same object that responds to the action of the drags. Differently from prior works that focused on repositioning objects, DragAPart predicts part-level interactions, such as opening and closing a drawer. We study this problem as a proxy for learning a generalist motion model, not restricted to a specific kinematic structure or object category. We start from a pre-trained image generator and fine-tune it on a new synthetic dataset, Drag-a-Move, which we introduce. Combined with a new encoding for the drags and dataset randomization, the model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding. |
| title | DragAPart: Learning a Part-Level Motion Prior for Articulated Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.15382 |