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Word art, which is text rendered with properly designed appealing artistic effects, has been a popular form of art throughout human history. Artistic text effects are of great aesthetic value and symbolic significance. Decorating with appropriate effects not only makes text more attractive but also significantly enhances the atmosphere of a scene. Thus, artistic text effects are widely used in publicity and advertising. Some text effects are simple, such as colors and shadows, while some can be complex, such as the burning flames in and exquisite decorations in. Manually creating vivid text effects requires lots of time and a series of complicated operations: observing the target glyphs, designing appropriate artistic effects, warping the texture to match the character shapes, and so on. It consumes hours of time even for well-trained designers. To produce word art more conveniently and efficiently, artistic text style transfer has been proposed recently to automatically render text with given artistic effects. [ABSTRACT FROM AUTHOR]

Arbitrary style transfer aims at migrating the style of a reference style painting to a target content image. Existing methods find it challenging to achieve good content fidelity and style migration at the same time. Moreover, they all rely on manually defined content and style, which is of limited universality and robustness. In this paper, we propose to introduce contrastive learning into style transfer, instructing the network to automatically learn to model the structural content and artistic style based on natural contrastive relationships in style transfer. Compared with existing methods, our learned modeling of content and style is more robust and universal. In addition, we further propose instance-wise contrastive style losses and a patch-wise contrastive content loss to guide style transfer. Combining the proposed contrastive losses and two self-reconstruction strategies, we develop a new style transfer framework, which is pluggable and can be flexibly applied to various style transfer modules. Experimental results demonstrate that our method has strong flexibility and synthesizes stylized images with higher quality. [ABSTRACT FROM AUTHOR]