  In this paper we introduce a novel Depth-Aware Video Saliency approach to
predict human focus of attention when viewing RGBD videos on regular 2D
screens. We train a generative convolutional neural network which predicts a
saliency map for a frame, given the fixation map of the previous frame.
Saliency estimation in this scenario is highly important since in the near
future 3D video content will be easily acquired and yet hard to display. This
can be explained, on the one hand, by the dramatic improvement of 3D-capable
acquisition equipment. On the other hand, despite the considerable progress in
3D display technologies, most of the 3D displays are still expensive and
require wearing special glasses. To evaluate the performance of our approach,
we present a new comprehensive database of eye-fixation ground-truth for RGBD
videos. Our experiments indicate that integrating depth into video saliency
calculation is beneficial. We demonstrate that our approach outperforms
state-of-the-art methods for video saliency, achieving 15% relative
improvement.
