  To truly understand the visual world our models should be able not only to
recognize images but also generate them. To this end, there has been exciting
recent progress on generating images from natural language descriptions. These
methods give stunning results on limited domains such as descriptions of birds
or flowers, but struggle to faithfully reproduce complex sentences with many
objects and relationships. To overcome this limitation we propose a method for
generating images from scene graphs, enabling explicitly reasoning about
objects and their relationships. Our model uses graph convolution to process
input graphs, computes a scene layout by predicting bounding boxes and
segmentation masks for objects, and converts the layout to an image with a
cascaded refinement network. The network is trained adversarially against a
pair of discriminators to ensure realistic outputs. We validate our approach on
Visual Genome and COCO-Stuff, where qualitative results, ablations, and user
studies demonstrate our method's ability to generate complex images with
multiple objects.
