  Generating new images with desired properties (e.g. new view/poses) from
source images has been enthusiastically pursued recently, due to its wide range
of potential applications. One way to ensure high-quality generation is to use
multiple sources with complementary information such as different views of the
same object. However, as source images are often misaligned due to the large
disparities among the camera settings, strong assumptions have been made in the
past with respect to the camera(s) or/and the object in interest, limiting the
application of such techniques. Therefore, we propose a new general approach
which models multiple types of variations among sources, such as view angles,
poses, facial expressions, in a unified framework, so that it can be employed
on datasets of vastly different nature. We verify our approach on a variety of
data including humans bodies, faces, city scenes and 3D objects. Both the
qualitative and quantitative results demonstrate the better performance of our
method than the state of the art.
