Few shot font generation via transferring similarity guided global style and quantization local style

by   Wei Pan, et al.

Automatic few-shot font generation (AFFG), aiming at generating new fonts with only a few glyph references, reduces the labor cost of manually designing fonts. However, the traditional AFFG paradigm of style-content disentanglement cannot capture the diverse local details of different fonts. So, many component-based approaches are proposed to tackle this problem. The issue with component-based approaches is that they usually require special pre-defined glyph components, e.g., strokes and radicals, which is infeasible for AFFG of different languages. In this paper, we present a novel font generation approach by aggregating styles from character similarity-guided global features and stylized component-level representations. We calculate the similarity scores of the target character and the referenced samples by measuring the distance along the corresponding channels from the content features, and assigning them as the weights for aggregating the global style features. To better capture the local styles, a cross-attention-based style transfer module is adopted to transfer the styles of reference glyphs to the components, where the components are self-learned discrete latent codes through vector quantization without manual definition. With these designs, our AFFG method could obtain a complete set of component-level style representations, and also control the global glyph characteristics. The experimental results reflect the effectiveness and generalization of the proposed method on different linguistic scripts, and also show its superiority when compared with other state-of-the-art methods. The source code can be found at https://github.com/awei669/VQ-Font.


page 3

page 9


Few-shot Font Generation with Weakly Supervised Localized Representations

Automatic few-shot font generation aims to solve a well-defined, real-wo...

XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation

Generating a new font library is a very labor-intensive and time-consumi...

Few-shot Font Generation with Localized Style Representations and Factorization

Automatic few-shot font generation is in high demand because manual desi...

Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts

A few-shot font generation (FFG) method has to satisfy two objectives: t...

BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer

In this work, we propose a Robust, Efficient, and Component-specific mak...

Few-shot Compositional Font Generation with Dual Memory

Generating a new font library is a very labor-intensive and time-consumi...

VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization

Few-shot font generation is challenging, as it needs to capture the fine...

Please sign up or login with your details

Forgot password? Click here to reset