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Controllable Artistic Text Style Transfer via Shape-Matching GAN

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arxiv 1905.01354 v2 pith:OWSON7TD submitted 2019-05-03 cs.CV

classification cs.CV
keywords styletexttransferartisticshapecontrolcontrollabledeformation
verification ladder T0 review T1 audit T2 compute T3 formal

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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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  1. AutoGAN: Neural Architecture Search for Generative Adversarial Networks

    cs.CV 2019-08 conditional novelty 7.0 of 10

    AutoGAN applies reinforcement-learning-based neural architecture search to GAN generators, discovering a CIFAR-10 architecture with FID 12.42 and an STL-10 FID 31.01, both state of the art in 2019.

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