title: DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
summary: 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_authors._id: 660108c1e73568bf2ee5a8aa
paper_authors.name: Ruining Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-26T07:54:26.576Z
paper_authors._id: 6467cc17e7a6a374fd1a41e5
paper_authors.avatarUrl: /avatars/3cbd4ad5bc17067ee38b13d0cbadd80c.svg
paper_authors.fullname: Ruining Li
paper_authors.type: user
paper_authors.user: rayli
paper_authors._id: 660108c1e73568bf2ee5a8ab
paper_authors.name: Chuanxia Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-25T11:30:09.059Z
paper_authors._id: 635e16911e2d8c6673e39785
paper_authors.avatarUrl: /avatars/905bccbe96b72862515ae4c27e7de642.svg
paper_authors.fullname: chuanxia
paper_authors.type: user
paper_authors.user: lyndonzheng
paper_authors._id: 660108c1e73568bf2ee5a8ac
paper_authors.name: Christian Rupprecht
paper_authors._id: 660108c1e73568bf2ee5a8ad
paper_authors.name: Andrea Vedaldi
paper_title: DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
paper_summary: 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_ai_keywords: DragAPart part-level interactions generalist motion model pre-trained image generator drag-a-move hash tables