  Recently, convolutional neural networks (CNN) have been successfully applied
to view synthesis problems. However, such CNN-based methods can suffer from
lack of texture details, shape distortions, or high computational complexity.
In this paper, we propose a novel CNN architecture for view synthesis called
"Deep View Morphing" that does not suffer from these issues. To synthesize a
middle view of two input images, a rectification network first rectifies the
two input images. An encoder-decoder network then generates dense
correspondences between the rectified images and blending masks to predict the
visibility of pixels of the rectified images in the middle view. A view
morphing network finally synthesizes the middle view using the dense
correspondences and blending masks. We experimentally show the proposed method
significantly outperforms the state-of-the-art CNN-based view synthesis method.
