Reproduce “CF-Font: Content Fusion for Few-shot Font Generation” on OpenResearch
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About this paper
Chi Wang, Min Zhou, Tiezheng Ge, Yuning Jiang, Hujun Bao, Weiwei Xu
Developed by researchers at Zhejiang University and Alibaba Group, CF-Font advances few-shot font generation by using a Content Fusion Module, Projected Character Loss, and Iterative Style-vector Refinement. It quantitatively outperforms existing state-of-the-art models on unseen logographic fonts, showing improvements of 5.7% in L1 and 5.0% in FID.
Key insights
- Dynamically fusing content features from multiple basis fonts leads to a more robust and adaptive content representation than relying on a single source, mitigating issues from imperfect disentanglement.
- Supervising global character shape and skeleton topology through 1D projections (Projected Character Loss) is effective for improving structural accuracy beyond pixel-level losses.
- A lightweight iterative refinement of the font-level style vector at inference time can significantly enhance the overall consistency and fidelity of generated fonts.
Summary by alphaXiv.
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