  Despite astonishing progress, generating realistic images of complex scenes
remains a challenging problem. Recently, layout-to-image synthesis approaches
have attracted much interest by conditioning the generator on a list of
bounding boxes and corresponding class labels. However, previous approaches are
very restrictive because the set of labels is fixed a priori. Meanwhile,
text-to-image synthesis methods have substantially improved and provide a
flexible way for conditional image generation. In this work, we introduce dense
text-to-image (DT2I) synthesis as a new task to pave the way toward more
intuitive image generation. Furthermore, we propose DTC-GAN, a novel method to
generate images from semantically rich region descriptions, and a multi-modal
region feature matching loss to encourage semantic image-text matching. Our
results demonstrate the capability of our approach to generate plausible images
of complex scenes using region captions.
