Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.

本文提出了基于无标注视频的 “物体部件、结构和动态模型”（PSD）模型，可以同时学习层次化、解耦合的物体表示以及物体部件的动态模型，并且在多个真实和合成数据集上都表现良好，实现了物体部件的分割、层次结构的构建以及运动分布的捕捉。