  We explore the problem of view synthesis from a narrow baseline pair of
images, and focus on generating high-quality view extrapolations with plausible
disocclusions. Our method builds upon prior work in predicting a multiplane
image (MPI), which represents scene content as a set of RGB$\alpha$ planes
within a reference view frustum and renders novel views by projecting this
content into the target viewpoints. We present a theoretical analysis showing
how the range of views that can be rendered from an MPI increases linearly with
the MPI disparity sampling frequency, as well as a novel MPI prediction
procedure that theoretically enables view extrapolations of up to $4\times$ the
lateral viewpoint movement allowed by prior work. Our method ameliorates two
specific issues that limit the range of views renderable by prior methods: 1)
We expand the range of novel views that can be rendered without depth
discretization artifacts by using a 3D convolutional network architecture along
with a randomized-resolution training procedure to allow our model to predict
MPIs with increased disparity sampling frequency. 2) We reduce the repeated
texture artifacts seen in disocclusions by enforcing a constraint that the
appearance of hidden content at any depth must be drawn from visible content at
or behind that depth. Please see our results video at:
https://www.youtube.com/watch?v=aJqAaMNL2m4.
