  There is a growing interest in developing computer vision methods that can
learn from limited supervision. In this paper, we consider the problem of
learning to predict camera viewpoints, where obtaining ground-truth annotations
are expensive and require special equipment, from a limited number of labeled
images. We propose a semi-supervised viewpoint estimation method that can learn
to infer viewpoint information from unlabeled image pairs, where two images
differ by a viewpoint change. In particular our method learns to synthesize the
second image by combining the appearance from the first one and viewpoint from
the second one. We demonstrate that our method significantly improves the
supervised techniques, especially in the low-label regime and outperforms the
state-of-the-art semi-supervised methods.
