DragAPart: Learning a Part-Level Motion Prior for Articulated Objects

Ruining Li, chuanxia, Christian Rupprecht, Andrea Vedaldi

DragAPart: Learning a Part-Level Motion Prior for Articulated Objects: 10 upvotes on Hugging Face Daily Papers, #7 of 13 papers on 2024-03-25. Day-by-day upvote history.

We introduce DragAPart, a method that, given an image and a set of drags as input, can generate a new image of the same object in a new state, compatible with 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. To this end, 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 new model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding.

Paper page on Hugging Face · arXiv

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