  In this paper, we tackle the problem of synthesizing a ground-view panorama
image conditioned on a top-view aerial image, which is a challenging problem
due to the large gap between the two image domains with different view-points.
Instead of learning cross-view mapping in a feedforward pass, we propose a
novel adversarial feedback GAN framework named PanoGAN with two key components:
an adversarial feedback module and a dual branch discrimination strategy.
First, the aerial image is fed into the generator to produce a target panorama
image and its associated segmentation map in favor of model training with
layout semantics. Second, the feature responses of the discriminator encoded by
our adversarial feedback module are fed back to the generator to refine the
intermediate representations, so that the generation performance is continually
improved through an iterative generation process. Third, to pursue
high-fidelity and semantic consistency of the generated panorama image, we
propose a pixel-segmentation alignment mechanism under the dual branch
discrimiantion strategy to facilitate cooperation between the generator and the
discriminator. Extensive experimental results on two challenging cross-view
image datasets show that PanoGAN enables high-quality panorama image generation
with more convincing details than state-of-the-art approaches. The source code
and trained models are available at \url{https://github.com/sswuai/PanoGAN}.
