# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 6 Conclusions  
We discovered that previous state-of-the-art methods for video style transfer have difficulties in finding the balance between temporal consistency and faithful style effects in their stylized videos. To obtain the balance, we revisited the gradient-based optimization methods using optical flow, and proposed solutions to their problems, including flow errors and a low-speed run time. Our solutions consist of a new initialization strategy with proposed mask techniques that is presented to reduce flow errors and constraints with proposed losses that are performed to ensure temporal consistency and image quality over long-range frames. Specifically, we propose a multi-scale mask fusion scheme to reduce the flow untraceable errors, an incremental mask to reduce ghosting artefacts and a multi-frame mask fusion technique to reduce the flow traceable errors. The initialized images obtained via our mask techniques increase the running speed from over 3 min per frame to less than 2 s per frame for gradient-based optimization algorithms. In addition, we also introduced multi-frame Coherent Losses to enhance the temporal consistency on both RGB-level and Feature-level, and Sharpness Losses to effectively mitigate the image blurriness artefacts over times. In the end, the ablation studies validated that each component of the proposed framework serves their purposes, and experiments on public datasets demonstrate that our approach achieves a better balance between temporal consistency and faithful style effects compared to state-of-the-art methods. We notice that the proposed method still needs several seconds for each frame to perform style transformation, which may be a little bit slower in practice. We will further investigate offline feed-forward networks to quickly approximate solutions to our style transformation problem in the near future.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14062630/s1](https://www.mdpi.com/article/10.3390/app14062630/s1)]([https://www.mdpi.com/article/10.3390/app14062630/s1](https://www.mdpi.com/article/10.3390/app14062630/s1)).  
Figure 19: User Study. It is noticed that [PERSON] et al. [36] received the most votes in terms of temporal consistency, but our proposed method obtained the most majority of votes for overall preference and style transformation effect compared to six other state-of-the-art methods. The number of votes for each method is consistent with Table 3 [30, 35, 36, 37, 38, 62].  
**Author Contributions:** Conceptualization, L.W. and X.Y.; methodology, L.W.; software, L.W.; validation, L.W. and X.Y.; formal analysis, L.W.; resources, L.W.; data curation, L.W.; writing--original draft preparation, L.W.; writing--review and editing, X.Y. and J.Z.; visualization, L.W.; supervision, X.Y. and J.Z.; project administration, X.Y.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
**Acknowledgments:** The authors would like to thank [PERSON], [PERSON], [PERSON] and [PERSON] for their helpful suggestions and discussions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.