Abstract
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.
Abstract (translated)
我们提出了神经网络3D关节构造前奏(NAP)，这是合成3D关节对象模型的第一种3D深度生成模型。尽管研究了生成3D物体、组合或场景的广泛研究，但仍缺乏关注捕捉关节对象分布的重点，这是人类和机器人交互的常见对象类别。生成关节对象，我们首先设计了一个 novel 关节树/图参数化，然后应用一个扩散除噪的probabilistic模型，在这个表示上，可以从随机完整图生成关节对象。为了捕捉 both the geometry 和运动结构，Whose distribution will affect each other，我们设计了图注意力除噪网络，以学习逆扩散过程。我们提出了一种新的距离，该距离适应广泛使用的3D生成度量任务，以评估生成质量，并实验表明我们在关节对象生成方面表现出高性能。我们还展示了多个条件生成应用，包括Part2Motion、PartNet-Imagination、Motion2Part和 GAPart2Object。
URL
https://arxiv.org/abs/2305.16315
