  We propose a data-driven approach for context-aware person image generation.
Specifically, we attempt to generate a person image such that the synthesized
instance can blend into a complex scene. In our method, the position, scale,
and appearance of the generated person are semantically conditioned on the
existing persons in the scene. The proposed technique is divided into three
sequential steps. At first, we employ a Pix2PixHD model to infer a coarse
semantic mask that represents the new person's spatial location, scale, and
potential pose. Next, we use a data-centric approach to select the closest
representation from a precomputed cluster of fine semantic masks. Finally, we
adopt a multi-scale, attention-guided architecture to transfer the appearance
attributes from an exemplar image. The proposed strategy enables us to
synthesize semantically coherent realistic persons that can blend into an
existing scene without altering the global context. We conclude our findings
with relevant qualitative and quantitative evaluations.
