  This paper studies the problem of view synthesis with certain amount of
rotations from a pair of images, what we called stereo unstructured
magnification. While the multi-plane image representation is well suited for
view synthesis with depth invariant, how to generalize it to unstructured views
remains a significant challenge. This is primarily due to the depth-dependency
caused by camera frontal parallel representation. Here we propose a novel
multiple homography image (MHI) representation, comprising of a set of scene
planes with fixed normals and distances. A two-stage network is developed for
novel view synthesis. Stage-1 is an MHI reconstruction module that predicts the
MHIs and composites layered multi-normal images along the normal direction.
Stage-2 is a normal-blending module to find blending weights. We also derive an
angle-based cost to guide the blending of multi-normal images by exploiting
per-normal geometry. Compared with the state-of-the-art methods, our method
achieves superior performance for view synthesis qualitatively and
quantitatively, especially for cases when the cameras undergo rotations.
