Controllable Artistic Text Style Transfer via Shape-Matching GAN

Shuai Yang, Zhangyang Wang, Zhaowen Wang, Ning Xu, Jiaying Liu, Zongming Guo; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 4442-4451

Abstract


Artistic text style transfer is the task of migrating the style from a source image to the target text to create artistic typography. Recent style transfer methods have considered texture control to enhance usability. However, controlling the stylistic degree in terms of shape deformation remains an important open challenge. In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter. Our key contribution is a novel bidirectional shape matching framework to establish an effective glyph-style mapping at various deformation levels without paired ground truth. Based on this idea, we propose a scale-controllable module to empower a single network to continuously characterize the multi-scale shape features of the style image and transfer these features to the target text. The proposed method demonstrates its superiority over previous state-of-the-arts in generating diverse, controllable and high-quality stylized text.

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[bibtex]
@InProceedings{Yang_2019_ICCV,
author = {Yang, Shuai and Wang, Zhangyang and Wang, Zhaowen and Xu, Ning and Liu, Jiaying and Guo, Zongming},
title = {Controllable Artistic Text Style Transfer via Shape-Matching GAN},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}