  Understanding how people explore immersive virtual environments is crucial
for many applications, such as designing virtual reality (VR) content,
developing new compression algorithms, or learning computational models of
saliency or visual attention. Whereas a body of recent work has focused on
modeling saliency in desktop viewing conditions, VR is very different from
these conditions in that viewing behavior is governed by stereoscopic vision
and by the complex interaction of head orientation, gaze, and other kinematic
constraints. To further our understanding of viewing behavior and saliency in
VR, we capture and analyze gaze and head orientation data of 169 users
exploring stereoscopic, static omni-directional panoramas, for a total of 1980
head and gaze trajectories for three different viewing conditions. We provide a
thorough analysis of our data, which leads to several important insights, such
as the existence of a particular fixation bias, which we then use to adapt
existing saliency predictors to immersive VR conditions. In addition, we
explore other applications of our data and analysis, including automatic
alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and
saliency-based compression.
