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Link to original content: https://doi.org/10.48550/arXiv.2203.07705
[2203.07705] APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2203.07705 (cs)
[Submitted on 15 Mar 2022]

Title:APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation

Authors:Yangming Shi, Haisong Ding, Kai Chen, Qiang Huo
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Abstract:Style-guided text image generation tries to synthesize text image by imitating reference image's appearance while keeping text content unaltered. The text image appearance includes many aspects. In this paper, we focus on transferring style image's background and foreground color patterns to the content image to generate photo-realistic text image. To achieve this goal, we propose 1) a content-style cross attention based pixel sampling approach to roughly mimicking the style text image's background; 2) a pixel-wise style modulation technique to transfer varying color patterns of the style image to the content image spatial-adaptively; 3) a cross attention based multi-scale style fusion approach to solving text foreground misalignment issue between style and content images; 4) an image patch shuffling strategy to create style, content and ground truth image tuples for training. Experimental results on Chinese handwriting text image synthesis with SCUT-HCCDoc and CASIA-OLHWDB datasets demonstrate that the proposed method can improve the quality of synthetic text images and make them more photo-realistic.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2203.07705 [cs.CV]
  (or arXiv:2203.07705v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2203.07705

Submission history

From: Yangming Shi [view email]
[v1] Tue, 15 Mar 2022 07:48:34 UTC (5,760 KB)
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