NAP: Neural 3D Articulation Prior
This addresses the lack of focus on articulated objects in 3D generation, benefiting robotics and human-computer interaction, though it is a novel method for a known bottleneck.
The paper tackles the problem of generating 3D articulated object models, which are common in human and robot interactions, by proposing Neural 3D Articulation Prior (NAP) as the first 3D deep generative model for this task, achieving high performance in articulated object generation as demonstrated in experiments.
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.