  We address the challenge of generating 3D articulated objects in a
controllable fashion. Currently, modeling articulated 3D objects is either
achieved through laborious manual authoring, or using methods from prior work
that are hard to scale and control directly. We leverage the interplay between
part shape, connectivity, and motion using a denoising diffusion-based method
with attention modules designed to extract correlations between part
attributes. Our method takes an object category label and a part connectivity
graph as input and generates an object's geometry and motion parameters. The
generated objects conform to user-specified constraints on the object category,
part shape, and part articulation. Our experiments show that our method
outperforms the state-of-the-art in articulated object generation, producing
more realistic objects while conforming better to user constraints.
  Video Summary at: http://youtu.be/cH_rbKbyTpE
