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 ap(cid:173) pearance mapping from examples by constructing a set of independently smooth interpolation networks; these networks can cover overlapping re(cid:173) gions of parameter space. A set growing procedure is used to find ex(cid:173) ample 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 param(cid:173) eter space where nearby examples have different appearances. We show results generating both simulated and real arm images.