  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.
