NAP: Neural 3D Articulation Prior
Jiahui Lei Congyue Deng Bokui Shen Leonidas Guibas Kostas Daniilidis
We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative
model to synthesize 3D articulated object models. Despite the extensive
research on generating 3D objects, compositions, or scenes, there remains a
lack of focus on capturing the distribution of articulated objects, a common
object category for human and robot interaction. To generate articulated
objects, we first design a novel articulation tree/graph parameterization and
then apply a diffusion-denoising probabilistic model over this representation
where articulated objects can be generated via denoising from random complete
graphs. In order to capture both the geometry and the motion structure whose
distribution will affect each other, we design a graph-attention denoising
network for learning the reverse diffusion process. We propose a novel distance
that adapts widely used 3D generation metrics to our novel task to evaluate
generation quality, and experiments demonstrate our high performance in
articulated object generation. We also demonstrate several conditioned
generation applications, including Part2Motion, PartNet-Imagination,
Motion2Part, and GAPart2Object.