  We present a novel method for inserting objects, specifically humans, into
existing images, such that they blend in a photorealistic manner, while
respecting the semantic context of the scene. Our method involves three
subnetworks: the first generates the semantic map of the new person, given the
pose of the other persons in the scene and an optional bounding box
specification. The second network renders the pixels of the novel person and
its blending mask, based on specifications in the form of multiple appearance
components. A third network refines the generated face in order to match those
of the target person. Our experiments present convincing high-resolution
outputs in this novel and challenging application domain. In addition, the
three networks are evaluated individually, demonstrating for example, state of
the art results in pose transfer benchmarks.
