LAUSR

In recent years, neural style transfer has attracted more and more attention, especially for image style transfer. However, temporally consistent style transfer for videos is still a challenging problem. Existing methods, either relying on a significant amount of video data with optical flows or using single-frame regularizers, fail to handle strong motions or complex variations, therefore have limited performance on real videos. In this article, we address the problem by jointly considering the intrinsic properties of stylization and temporal consistency. We first identify the cause of the conflict between style transfer and temporal consistency, and propose to reconcile this contradiction by relaxing the objective function, so as to make the stylization loss term more robust to motions. Through relaxation, style transfer is more robust to inter-frame variation without degrading the subjective effect. Then, we provide a novel formulation and understanding of temporal consistency. Based on the formulation, we analyze the drawbacks of existing training strategies and derive a new regularization. We show by experiments that the proposed regularization can better balance the spatial and temporal performance. Based on relaxation and regularization, we design a zero-shot video style transfer framework. Moreover, for better feature migration, we introduce a new module to dynamically adjust inter-channel distributions. Quantitative and qualitative results demonstrate the superiority of our method over state-of-the-art style transfer methods. Our project is publicly available at: https://daooshee.github.io/ReReVST/.