  We address the problem of novel view synthesis: given an input image,
synthesizing new images of the same object or scene observed from arbitrary
viewpoints. We approach this as a learning task but, critically, instead of
learning to synthesize pixels from scratch, we learn to copy them from the
input image. Our approach exploits the observation that the visual appearance
of different views of the same instance is highly correlated, and such
correlation could be explicitly learned by training a convolutional neural
network (CNN) to predict appearance flows -- 2-D coordinate vectors specifying
which pixels in the input view could be used to reconstruct the target view.
Furthermore, the proposed framework easily generalizes to multiple input views
by learning how to optimally combine single-view predictions. We show that for
both objects and scenes, our approach is able to synthesize novel views of
higher perceptual quality than previous CNN-based techniques.
