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Few-shot font generation, especially for Chinese calligraphy fonts, is achallenging and ongoing problem. With the help of prior knowledge that ismainly based on glyph consistency assumptions, some recently proposed methodscan synthesize high-quality Chinese glyph images. However, glyphs incalligraphy font styles often do not meet these assumptions. To address thisproblem, we propose a novel model, DeepCalliFont, for few-shot Chinesecalligraphy font synthesis by integrating dual-modality generative models.Specifically, the proposed model consists of image synthesis and sequencegeneration branches, generating consistent results via a dual-modalityrepresentation learning strategy. The two modalities (i.e., glyph images andwriting sequences) are properly integrated using a feature recombination moduleand a rasterization loss function. Furthermore, a new pre-training strategy isadopted to improve the performance by exploiting large amounts of uni-modalitydata. Both qualitative and quantitative experiments have been conducted todemonstrate the superiority of our method to other state-of-the-art approachesin the task of few-shot Chinese calligraphy font synthesis. The source code canbe found at https://github.com/lsflyt-pku/DeepCalliFont.

DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models