| |
Abstract:
We present a method for learning complex appearance mappings,
such as occur with images of articulated objects. Traditional
interpolation networks fail on this case since appearance is not
necessarily a smooth function nor a linear manifold for articulated
objects. We define an appearance mapping from examples by
constructing a set of independently smooth interpolation networks;
these networks can cover overlapping regions of parameter space. A
set growing procedure is used to find example clusters which are
well-approximated within their convex hull; interpolation then
proceeds only within these sets of examples. With this method
physically valid images are produced even in regions of parameter
space where nearby examples have different appearances. We show
results generating both simulated and real arm images.
|
