  Taking an image of an object is at its core a lossy process. The rich
information about the three-dimensional structure of the world is flattened to
an image plane and decisions such as viewpoint and camera parameters are final
and not easily revertible. As a consequence, possibilities of changing
viewpoint are limited. Given a single image depicting an object, novel-view
synthesis is the task of generating new images that render the object from a
different viewpoint than the one given. The main difficulty is to synthesize
the parts that are disoccluded; disocclusion occurs when parts of an object are
hidden by the object itself under a specific viewpoint. In this work, we show
how to improve novel-view synthesis by making use of the correlations observed
in 3D models and applying them to new image instances. We propose a technique
to use the structural information extracted from a 3D model that matches the
image object in terms of viewpoint and shape. For the latter part, we propose
an efficient 2D-to-3D alignment method that associates precisely the image
appearance with the 3D model geometry with minimal user interaction. Our
technique is able to simulate plausible viewpoint changes for a variety of
object classes within seconds. Additionally, we show that our synthesized
images can be used as additional training data that improves the performance of
standard object detectors.
