MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font Generation

• URL: http://arxiv.org/abs/2503.02799v1
• Date: Tue, 04 Mar 2025 17:18:43 GMT
• Title: MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font Generation
• Authors: Weihang Wang, Duolin Sun, Jielei Zhang, Longwen Gao,
• Abstract summary: Few-shot Font Generation (FFG) aims to create new font libraries using limited reference glyphs.<n> MX-Font considers the content of a character from the perspective of a local component, employing a Mixture of Experts (MoE) approach to adaptively extract the component for better transition.<n>To alleviate these problems, we propose Heterogeneous Aggregation Experts (HAE), a powerful feature extraction expert that helps decouple content and style downstream from being able to aggregate information in channel and spatial dimensions.
• Score: 4.850415618396122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot Font Generation (FFG) aims to create new font libraries using limited reference glyphs, with crucial applications in digital accessibility and equity for low-resource languages, especially in multilingual artificial intelligence systems. Although existing methods have shown promising performance, transitioning to unseen characters in low-resource languages remains a significant challenge, especially when font glyphs vary considerably across training sets. MX-Font considers the content of a character from the perspective of a local component, employing a Mixture of Experts (MoE) approach to adaptively extract the component for better transition. However, the lack of a robust feature extractor prevents them from adequately decoupling content and style, leading to sub-optimal generation results. To alleviate these problems, we propose Heterogeneous Aggregation Experts (HAE), a powerful feature extraction expert that helps decouple content and style downstream from being able to aggregate information in channel and spatial dimensions. Additionally, we propose a novel content-style homogeneity loss to enhance the untangling. Extensive experiments on several datasets demonstrate that our MX-Font++ yields superior visual results in FFG and effectively outperforms state-of-the-art methods. Code and data are available at https://github.com/stephensun11/MXFontpp.

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