  Image style transfer models based on convolutional neural networks usually
suffer from high temporal inconsistency when applied to videos. Some video
style transfer models have been proposed to improve temporal consistency, yet
they fail to guarantee fast processing speed, nice perceptual style quality and
high temporal consistency at the same time. In this paper, we propose a novel
real-time video style transfer model, ReCoNet, which can generate temporally
coherent style transfer videos while maintaining favorable perceptual styles. A
novel luminance warping constraint is added to the temporal loss at the output
level to capture luminance changes between consecutive frames and increase
stylization stability under illumination effects. We also propose a novel
feature-map-level temporal loss to further enhance temporal consistency on
traceable objects. Experimental results indicate that our model exhibits
outstanding performance both qualitatively and quantitatively.
