  Replacing the background and simultaneously adjusting foreground objects is a
challenging task in image editing. Current techniques for generating such
images relies heavily on user interactions with image editing softwares, which
is a tedious job for professional retouchers. To reduce their workload, some
exciting progress has been made on generating images with a given background.
However, these models can neither adjust the position and scale of the
foreground objects, nor guarantee the semantic consistency between foreground
and background. To overcome these limitations, we propose a framework --
ART(Auto-Retoucher), to generate images with sufficient semantic and spatial
consistency. Images are first processed by semantic matting and scene parsing
modules, then a multi-task verifier model will give two confidence scores for
the current background and position setting. We demonstrate that our jointly
optimized verifier model successfully improves the visual consistency, and our
ART framework performs well on images with the human body as foregrounds.
