__Abstract:__**Optimization is an appealing way to compute
the motion of an animated character because it allows the user to specify
the desired motion in a sparse, intuitive way. The difficulty of solving
this problem for complex characters such as humans is due in part to the
high dimensionality of the search space. The dimensionality is an
artifact of the problem representation because most dynamic human behaviors
are intrinsically low dimensional with, for example, legs and arms operating
in a coordinated way. We describe a method that exploits this observation
to create an optimization problem that is easier to solve. Our method
utilizes an existing motion capture database to find a low-dimensional
space that captures the properties of the desired behavior. We show that
when the optimization problem is solved within this low-dimensional subspace,
a sparse sketch can be used as an initial guess and full physics constraints
can be enabled. We demonstrate the power of our approach with examples
of forward, vertical, and turning jumps; with running and walking; and
with several acrobatic flips.**