Generalized W-Net: Arbitrary-style Chinese Character Synthesization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911912462123008 |
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| author | Jiang, Haochuan Yang, Guanyu Cheng, Fei Huang, Kaizhu |
| author_facet | Jiang, Haochuan Yang, Guanyu Cheng, Fei Huang, Kaizhu |
| contents | Synthesizing Chinese characters with consistent style using few stylized examples is challenging. Existing models struggle to generate arbitrary style characters with limited examples. In this paper, we propose the Generalized W-Net, a novel class of W-shaped architectures that addresses this. By incorporating Adaptive Instance Normalization and introducing multi-content, our approach can synthesize Chinese characters in any desired style, even with limited examples. It handles seen and unseen styles during training and can generate new character contents. Experimental results demonstrate the effectiveness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalized W-Net: Arbitrary-style Chinese Character Synthesization Jiang, Haochuan Yang, Guanyu Cheng, Fei Huang, Kaizhu Computer Vision and Pattern Recognition Synthesizing Chinese characters with consistent style using few stylized examples is challenging. Existing models struggle to generate arbitrary style characters with limited examples. In this paper, we propose the Generalized W-Net, a novel class of W-shaped architectures that addresses this. By incorporating Adaptive Instance Normalization and introducing multi-content, our approach can synthesize Chinese characters in any desired style, even with limited examples. It handles seen and unseen styles during training and can generate new character contents. Experimental results demonstrate the effectiveness of our approach. |
| title | Generalized W-Net: Arbitrary-style Chinese Character Synthesization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.06847 |