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.

本文提出了两种计算机视觉算法对图像和视频进行风格迁移，第一种是根据 Gatys 算法进行能量最小化的图像风格转移技术，针对图像中的规模和遮挡等问题提出了改进，第二种是将视频风格迁移建模为一个学习问题，使用深度学习网络和新的训练程序使其在几乎实时的情况下实现任意长度的视频稳定风格转移，最后还介绍了如何将这些方法应用于 360 度图像和视频。