  A single panel of a comic book can say a lot: it can depict not only where
the characters currently are, but also their motions, their motivations, their
emotions, and what they might do next. More generally, humans routinely infer
complex sequences of past and future events from a *static snapshot* of a
*dynamic scene*, even in situations they have never seen before.
  In this paper, we model how humans make such rapid and flexible inferences.
Building on a long line of work in cognitive science, we offer a Monte Carlo
algorithm whose inferences correlate well with human intuitions in a wide
variety of domains, while only using a small, cognitively-plausible number of
samples. Our key technical insight is a surprising connection between our
inference problem and Monte Carlo path tracing, which allows us to apply
decades of ideas from the computer graphics community to this
seemingly-unrelated theory of mind task.
