  Manually re-drawing an image in a certain artistic style takes a professional
artist a long time. Doing this for a video sequence single-handedly is beyond
imagination. We present two computational approaches that transfer the style
from one image (for example, a painting) to a whole video sequence. In our
first approach, we adapt to videos the original image style transfer technique
by Gatys et al. based on energy minimization. We introduce new ways of
initialization and new loss functions to generate consistent and stable
stylized video sequences even in cases with large motion and strong occlusion.
Our second approach formulates video stylization as a learning problem. We
propose a deep network architecture and training procedures that allow us to
stylize arbitrary-length videos in a consistent and stable way, and nearly in
real time. We show that the proposed methods clearly outperform simpler
baselines both qualitatively and quantitatively. Finally, we propose a way to
adapt these approaches also to 360 degree images and videos as they emerge with
recent virtual reality hardware.
