Modeling the appearance of 3D objects under very large pose variations relies on recovering correspondence between local features and the texture variations across views. However, changes in object 3D pose introduce self-occlusions and cause nonlinear variations in both the shape and the texture of object appearance. In this paper, we introduce a pose-invariant appearance model that utilizes a generic-view shape template for alignment, Kernel PCA for modeling shape and texture non-linearities across views of large pose variations, and a neural network for model fitting to new images.