Fast continuous patch-based artistic style transfer for videos.

Convolutional neural network-based image style transfer models often suffer from temporal inconsistency when applied to video. Although several video style transfer models have been proposed to improve temporal consistency, they often trade off processing speed, perceptual style quality, and temporal consistency. In this work, we propose a novel approach for fast continuous patch-based arbitrary video style transfer that achieves high-quality transfer results while maintaining temporal coherence. Our approach begins with stylizing the first frame as a standalone single image using patch propagation within the content activation. Subsequent frames are computed based on the key insight that optical flow field evaluated from neighboring content activations provides meaningful information to preserve temporal coherence efficiently. To address the problems introduced from optical flow stage, we additionally incorporate a correction procedure as a post-process to ensure a high-quality stylized video. Finally, we demonstrate our method can transfer arbitrary styles on a set of examples and illustrate that our approach exhibits superior performance both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]