  Cross-view image generation has been recently proposed to generate images of
one view from another dramatically different view. In this paper, we
investigate exocentric (third-person) view to egocentric (first-person) view
image generation. This is a challenging task since egocentric view sometimes is
remarkably different from exocentric view. Thus, transforming the appearances
across the two views is a non-trivial task. To this end, we propose a novel
Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss
to learn the shared information for generating egocentric images from
exocentric view. We also incorporate a novel contextual feature loss in the
learning procedure to capture the contextual information in images. Extensive
experiments on the Exo-Ego datasets show that our model outperforms the
state-of-the-art approaches.
