DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
<|reference_start|>DragAPart: Learning a Part-Level Motion Prior for Articulated Objects: 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.<|reference_end|>
@article{li2024dragapart:,
    title={DragAPart: Learning a Part-Level Motion Prior for Articulated Objects},
    author={Ruining Li, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi},
    journal={arXiv preprint arXiv:2403.15382},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15382},
    primaryClass={cs.CV}
}