We propose a novel unsupervised method to learn the pose and part-segmentation of articulated objects with rigid parts. Given two observations of an object in different articulation states, our method learns the geometry and appearance of object parts by using an implicit model from the first observation, distils the part segmentation and articulation from the second observation while rendering the latter observation. Additionally, to tackle the complexities in the joint optimization of part segmentation and articulation, we propose a voxel grid-based initialization strategy and a decoupled optimization procedure. Compared to the prior unsupervised work, our model obtains significantly better performance, and generalizes to objects with multiple parts while it can be efficiently from few views for the latter observation.

翻译：我们提出了一种新颖的无监督方法，用于学习具有刚性部件的铰接物体的姿态和部件分割。给定物体在不同铰接状态下的两次观测，我们的方法利用第一次观测的隐式模型学习物体部件的几何与外观，在渲染第二次观测的同时，从中提取部件分割与铰接信息。此外，为应对部件分割与铰接联合优化的复杂性，我们提出了一种基于体素网格的初始化策略和解耦优化流程。与先前的无监督工作相比，我们的模型获得了显著更优的性能，并能泛化至具有多个部件的物体，同时仅需少量第二次观测视图即可高效完成建模。