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.