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.

DragAPart 是一种基于图像和一组拖动操作输入的方法，可以生成一个物体在新状态下的新图像，与拖动操作相匹配。与以前侧重于重新定位物体的工作不同，DragAPart 预测部分级别的互动，例如打开和关闭抽屉。通过将预训练的图像生成器用新的合成数据集 Drag-a-Move 和新的拖动编码进行微调，并结合数据集随机化，该模型在实际图像和不同类别上具有很好的推广能力。与之前的运动控制生成器相比，我们展示了更好的部分级别的运动理解能力。