Temporal Consistent Oil Painting Video Stylization

Luyao Zhang, Wenjing Wang, Jiaying Liu

Abstract: The automatic rendering of oil painting style video has great artistic and commercial application value. Temporal consistency is the bottleneck of video rendering. However, existing translation methods are either designed for images, or have high training/inference costs on videos due to the estimation of optical flows. This paper explores how to render videos in oil painting styles without video training data. We adopt a motion-based regularization in the training phase and a feature statistics sharing strategy in the inference phase. Experiments show that our model can render vivid and temporally smooth oil painting videos.