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$$^\circ $$∘ images and videos as they emerge with recent virtual reality hardware.

中文翻译：

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视频和球形图像的艺术风格转换

以某种艺术风格手动重新绘制图像需要专业艺术家很长时间。单枪匹马地为一个视频序列做这件事是超乎想象的。我们提出了两种计算方法，将风格从一张图像（例如，一幅画）转移到整个视频序列。在我们的第一种方法中，我们将 Gatys 等人的原始图像风格转移技术应用于视频。基于能量最小化。我们引入了新的初始化方法和新的损失函数，即使在大运动和强遮挡的情况下，也能生成一致且稳定的风格化视频序列。我们的第二种方法将视频风格化作为一个学习问题。我们提出了一种深度网络架构和训练程序，使我们能够以一致和稳定的方式几乎实时地对任意长度的视频进行风格化。我们表明，所提出的方法在定性和定量上明显优于更简单的基线。最后，我们提出了一种方法，使这些方法也适用于 360$$^\circ $$∘ 图像和视频，因为它们出现在最近的虚拟现实硬件中。