  This paper presents the first attempt at stereoscopic neural style transfer,
which responds to the emerging demand for 3D movies or AR/VR. We start with a
careful examination of applying existing monocular style transfer methods to
left and right views of stereoscopic images separately. This reveals that the
original disparity consistency cannot be well preserved in the final
stylization results, which causes 3D fatigue to the viewers. To address this
issue, we incorporate a new disparity loss into the widely adopted style loss
function by enforcing the bidirectional disparity constraint in non-occluded
regions. For a practical real-time solution, we propose the first feed-forward
network by jointly training a stylization sub-network and a disparity
sub-network, and integrate them in a feature level middle domain. Our disparity
sub-network is also the first end-to-end network for simultaneous bidirectional
disparity and occlusion mask estimation. Finally, our network is effectively
extended to stereoscopic videos, by considering both temporal coherence and
disparity consistency. We will show that the proposed method clearly
outperforms the baseline algorithms both quantitatively and qualitatively.
