The Transfer of Film Style Based on Meta-Learning
Keqin Chen, Yixin Meng, Gongfan Zhang, Kun Zhu, Wenbin Luo · 2019
The film style refers to the combination and configuration of colors in the film, which is often dominated by one color, making the picture show a certain tendency. However, the creation of special effects not only requires special professional skills, but also takes a lot of manual labor. If artificial intelligence technology can be transferred to the picture style of the film industry, production costs will be greatly reduced. In this paper, we propose a technique which combines the style transfer and meta-learning to create a new way of thinking. Compared with traditional image style transfer, the transfer of film style based on meta-learning could save the cost of film production significantly and take much less time to perform the transfer process. Toward the end, extensive experimental results were presented to validate our proposed method, which clearly outperforms the traditional image style transfer.