Font Representation Learning via Paired-glyph Matching
Fonts can convey profound meanings of words in various forms of glyphs. Without typography knowledge, manually selecting an appropriate font or designing a new font is a tedious and painful task. To allow users to explore vast font styles and create new font styles, font retrieval and font style transfer methods have been proposed. These tasks increase the need for learning high-quality font representations. Therefore, we propose a novel font representation learning scheme to embed font styles into the latent space. For the discriminative representation of a font from others, we propose a paired-glyph matching-based font representation learning model that attracts the representations of glyphs in the same font to one another, but pushes away those of other fonts. Through evaluations on font retrieval with query glyphs on new fonts, we show our font representation learning scheme achieves better generalization performance than the existing font representation learning techniques. Finally on the downstream font style transfer and generation tasks, we confirm the benefits of transfer learning with the proposed method. The source code is available at https://github.com/junhocho/paired-glyph-matching.
Code (1)
Tasks
Font Style TransferRepresentation LearningRetrievalStyle TransferTransfer LearningSimilar Papers 제목 키워드 기반
DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation
Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevitable quality losses after vectorization.…
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-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which are expected to have similar styles. How…
Font GenerationQuantizationFew-Shot Font Generation by Learning Fine-Grained Local Styles
Few-shot font generation (FFG), which aims to generate a new font with a few examples, is gaining increasing attention due to the significant reduction in labor cost. A typical FFG pipeline considers characters in a stan…
Font GenerationXMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation
Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Few-shot font generation is thus required, as it requires only a few glyph references without fine-tuning during test…
DisentanglementFont GenerationLearning Implicit Glyph Shape Representation
In this paper, we present a novel implicit glyph shape representation, which models glyphs as shape primitives enclosed by quadratic curves, and naturally enables generating glyph images at arbitrary high resolutions. Ex…
Font Style TransferStyle TransferVector Graphics