  The goal of semantic image synthesis is to generate photo-realistic images
from semantic label maps. It is highly relevant for tasks like content
generation and image editing. Current state-of-the-art approaches, however,
still struggle to generate realistic objects in images at various scales. In
particular, small objects tend to fade away and large objects are often
generated as collages of patches. In order to address this issue, we propose a
Dual Pyramid Generative Adversarial Network (DP-GAN) that learns the
conditioning of spatially-adaptive normalization blocks at all scales jointly,
such that scale information is bi-directionally used, and it unifies
supervision at different scales. Our qualitative and quantitative results show
that the proposed approach generates images where small and large objects look
more realistic compared to images generated by state-of-the-art methods.
