  Understanding and modeling the dynamics of human gaze behavior in 360$^\circ$
environments is a key challenge in computer vision and virtual reality.
Generative adversarial approaches could alleviate this challenge by generating
a large number of possible scanpaths for unseen images. Existing methods for
scanpath generation, however, do not adequately predict realistic scanpaths for
360$^\circ$ images. We present ScanGAN360, a new generative adversarial
approach to address this challenging problem. Our network generator is tailored
to the specifics of 360$^\circ$ images representing immersive environments.
Specifically, we accomplish this by leveraging the use of a spherical
adaptation of dynamic-time warping as a loss function and proposing a novel
parameterization of 360$^\circ$ scanpaths. The quality of our scanpaths
outperforms competing approaches by a large margin and is almost on par with
the human baseline. ScanGAN360 thus allows fast simulation of large numbers of
virtual observers, whose behavior mimics real users, enabling a better
understanding of gaze behavior and novel applications in virtual scene design.
