  Video style transfer is a useful component for applications such as augmented
reality, non-photorealistic rendering, and interactive games. Many existing
methods use optical flow to preserve the temporal smoothness of the synthesized
video. However, the estimation of optical flow is sensitive to occlusions and
rapid motions. Thus, in this work, we introduce a novel evolve-sync loss
computed by evolvements to replace optical flow. Using this evolve-sync loss,
we build an adversarial learning framework, termed as Video Style Transfer
Generative Adversarial Network (VST-GAN), which improves upon the MGAN method
for image style transfer for more efficient video style transfer. We perform
extensive experimental evaluations of our method and show quantitative and
qualitative improvements over the state-of-the-art methods.
