  Wooden blocks are a common toy for infants, allowing them to develop motor
skills and gain intuition about the physical behavior of the world. In this
paper, we explore the ability of deep feed-forward models to learn such
intuitive physics. Using a 3D game engine, we create small towers of wooden
blocks whose stability is randomized and render them collapsing (or remaining
upright). This data allows us to train large convolutional network models which
can accurately predict the outcome, as well as estimating the block
trajectories. The models are also able to generalize in two important ways: (i)
to new physical scenarios, e.g. towers with an additional block and (ii) to
images of real wooden blocks, where it obtains a performance comparable to
human subjects.
