![]() Utilize multiple unpaired samples to align the feature distributions belonging ![]() Target and strengthen the coarse-grained understanding of character content, we To be specific, a few paired samples from differentĬharacter styles are leveraged to attain a fine-grained correlation between Redundant preprocessing to generate fine-grained target-style characters withįew-shot references. This paper, we propose a simple but powerful end-to-end Chinese calligraphyįont generation framework ZiGAN, which does not require any manual operation or The character into different parts to be learned and transferred separately. Recently, several GAN-based methods haveīeen proposed for font synthesis, but some of them require numerous referenceĭata and the other part of them have cumbersome preprocessing steps to divide ![]() Handwriting of calligraphy masters has a more irregular stroke and is difficult Download a PDF of the paper titled ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach, by Qi Wen and 3 other authors Download PDF Abstract: Chinese character style transfer is a very challenging problem because of theĬomplexity of the glyph shapes or underlying structures and large numbers ofĮxisted characters, when comparing with English letters.
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