Style-woven Attention Network for Zero-shot Ink Wash Painting Style Transfer

Traditional Chinese painting is a unique form of artistic expression. Compared with western art painting, it pays more attention to the verve in visual effect, especially ink painting, which makes good use of lines and pays little attention to information such as texture. Some style transfer methods have recently begun to apply traditional Chinese painting style (such as ink wash style) to photorealistic. Ink stylization of different types of real-world photos in a dataset using these style transfer methods has some limitations. When the input images are animal types that have not been seen in the training set, the generated results retain some semantic features of the data in the training set, resulting in distortion. Therefore, in this paper, we attempt to separate the feature representations for styles and contents and propose a style-woven attention network to achieve zero-shot ink wash painting style transfer. Our model learns to disentangle the data representations in an unsupervised fashion and capture the semantic correlations of content and style. In addition, an ink style loss is added to improve the learning ability of the style encoder. In order to verify the ability of ink wash stylization, we augmented the publicly available dataset $ChipPhi$. Extensive experiments based on a wide validation set prove that our method achieves state-of-the-art results.