Objects are made of parts, each with distinct geometry, physics,
functionality, and affordances. Developing such a distributed, physical,
interpretable representation of objects will facilitate intelligent agents to
better explore and interact with the world. In this paper, we study physical
primitive decomposition---understanding an object through its components, each
with physical and geometric attributes. As annotated data for object parts and
physics are rare, we propose a novel formulation that learns physical
primitives by explaining both an object's appearance and its behaviors in
physical events. Our model performs well on block towers and tools in both
synthetic and real scenarios; we also demonstrate that visual and physical
observations often provide complementary signals. We further present ablation
and behavioral studies to better understand our model and contrast it with
human performance.Comment: ECCV 2018. Project page: http://ppd.csail.mit.edu