  Interactive image synthesis from user-guided input is a challenging task when
users wish to control the scene structure of a generated image with
ease.Although remarkable progress has been made on layout-based image synthesis
approaches, in order to get realistic fake image in interactive scene, existing
methods require high-precision inputs, which probably need adjustment several
times and are unfriendly to novice users. When placement of bounding boxes is
subject to perturbation, layout-based models suffer from "missing regions" in
the constructed semantic layouts and hence undesirable artifacts in the
generated images. In this work, we propose Panoptic Layout Generative
Adversarial Networks (PLGAN) to address this challenge. The PLGAN employs
panoptic theory which distinguishes object categories between "stuff" with
amorphous boundaries and "things" with well-defined shapes, such that stuff and
instance layouts are constructed through separate branches and later fused into
panoptic layouts. In particular, the stuff layouts can take amorphous shapes
and fill up the missing regions left out by the instance layouts. We
experimentally compare our PLGAN with state-of-the-art layout-based models on
the COCO-Stuff, Visual Genome, and Landscape datasets. The advantages of PLGAN
are not only visually demonstrated but quantitatively verified in terms of
inception score, Fr\'echet inception distance, classification accuracy score,
and coverage.
