  In-depth scene descriptions and question answering tasks have greatly
increased the scope of today's definition of scene understanding. While such
tasks are in principle open ended, current formulations primarily focus on
describing only the current state of the scenes under consideration. In
contrast, in this paper, we focus on the future states of the scenes which are
also conditioned on actions. We posit this as a question answering task, where
an answer has to be given about a future scene state, given observations of the
current scene, and a question that includes a hypothetical action. Our solution
is a hybrid model which integrates a physics engine into a question answering
architecture in order to anticipate future scene states resulting from
object-object interactions caused by an action. We demonstrate first results on
this challenging new problem and compare to baselines, where we outperform
fully data-driven end-to-end learning approaches.
