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

Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction