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[bibtex]@InProceedings{Chen_2021_CVPR, author = {Chen, Haibo and Zhao, Lei and Wang, Zhizhong and Zhang, Huiming and Zuo, Zhiwen and Li, Ailin and Xing, Wei and Lu, Dongming}, title = {DualAST: Dual Style-Learning Networks for Artistic Style Transfer}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {872-881} }
DualAST: Dual Style-Learning Networks for Artistic Style Transfer
Abstract
Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles. Existing methods learn styles from either a single style example or a collection of artworks. Accordingly, the stylization results are either inferior in visual quality or limited in style controllability. To tackle this problem, we propose a novel Dual Style-Learning Artistic Style Transfer (DualAST) framework to learn simultaneously both the holistic artist-style (from a collection of artworks) and the specific artwork-style (from a single style image): the artist-style sets the tone (i.e., the overall feeling) for the stylized image, while the artwork-style determines the details of the stylized image, such as color and texture. Moreover, we introduce a Style-Control Block (SCB) to adjust the styles of generated images with a set of learnable style-control factors. We conduct extensive experiments to evaluate the performance of the proposed framework, the results of which confirm the superiority of our method.
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