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Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via oneshot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.

Retinex model is widely adopted in various low-light image enhancement tasks. The basic idea of the Retinex theory is to decompose images into reflectance and illumination. The ill-posed decomposition is usually handled by hand-crafted constraints and priors. With the recently emerging deep-learning based approaches as tools, in this paper, we integrate the idea of Retinex decomposition and semantic information awareness. Based on the observation that various objects and backgrounds have different material, reflection and perspective attributes, regions of a single low-light image may require different adjustment and enhancement regarding contrast, illumination and noise. We propose an enhancement pipeline with three parts that effectively utilize the semantic layer information. Specifically, we extract the segmentation, reflectance as well as illumination layers, and concurrently enhance every separate region,. . sky, ground and objects for outdoor scenes. Extensive experiments on both synthetic data and real world images demonstrate the superiority of our method over current state-ofthe-art low-light enhancement algorithms. Our code will be public available at: https://mm20-semanticreti.github.io/. CCS CONCEPTS • Computing methodologies → Image manipulation.

Text effects are combinations of visual elements such as outlines, colors and textures of text, which can dramatically improve its artistry. Although text effects are extensively utilized in the design industry, they are usually created by human experts due to their extreme complexity, which is laborious and not practical for normal users. In recent years, some efforts have been made for automatic text effects transfer, however, the lack of data limits the capability of transfer models. To address this problem, we introduce a new text effects dataset, TE141K, with 141,081 text effects/glyph pairs in total. Our dataset consists of 152 professionally designed text effects, rendered on glyphs including English letters, Chinese characters, Arabic numerals, etc. To the best of our knowledge, this is the largest dataset for text effects transfer as far. Based on this dataset, we propose a baseline approach named Text Effects Transfer GAN (TET-GAN), which supports the transfer of all 152 styles in one model and can efficiently extend to new styles. Finally, we conduct a comprehensive comparison where 14 style transfer models are benchmarked. Experimental results demonstrate the superiority of TET-GAN both qualitatively and quantitatively, and indicate that our dataset is effective and challenging. Index Terms-Text effects, style transfer, deep neural network, large-scale dataset, model benchmarking. ! arXiv:1905.03646v2 [cs.CV]

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Fashion manipulation has attracted growing interest due to its great application value, which inspires many researches towards fashion images. However, little attention has been paid to fashion design draft. In this paper, we study a new unaligned translation problem between design drafts and real fashion items, whose main challenge lies in the huge misalignment between the two modalities. We first collect paired design drafts and real fashion item images without pixel-wise alignment. To solve the misalignment problem, our main idea is to train a sampling network to adaptively adjust the input to an intermediate state with structure alignment to the output. Moreover, built upon the sampling network, we present design draft to real fashion item translation network (D2RNet), where two separate translation streams that focus on texture and shape, respectively, are combined tactfully to get both benefits. D2RNet is able to generate realistic garments with both texture and shape consistency to their design drafts. We show that this idea can be effectively applied to the reverse translation problem and present R2DNet accordingly. Extensive experiments on unaligned fashion design translation demonstrate the superiority of our method over state-of-the-art methods. Our project website is available at: https: //victoriahy.github.io/MM2020/.

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