Few-Shot Font Generation With Weakly Supervised Localized Representations.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Computational Linguistics

Background:

• Manual font design is costly and requires expert knowledge.
• Existing universal style representations struggle with complex scripts like Chinese characters.
• Few-shot font generation needs efficient methods for diverse scripts.

Purpose of the Study:

• To develop a novel font generation method capable of learning localized styles.
• To address the limitations of universal style representations in complex character systems.
• To improve few-shot font generation accuracy with minimal reference glyphs.

Main Methods:

• Proposed a component-wise style representation instead of universal styles.
• Utilized a product of component and style factors inspired by low-rank matrix factorization.
• Developed a method for few-shot font generation without strong locality supervision.

Main Results:

• Achieved significantly better few-shot font generation results with only eight reference glyphs.
• Demonstrated effective synthesis of complex local details in text designs.
• Outperformed state-of-the-art methods in few-shot font generation tasks.

Conclusions:

• Component-wise style representations are effective for complex scripts.
• Low-rank factorization enables efficient learning of localized styles.
• The proposed method advances few-shot font generation for diverse writing systems.