Few-shot Font Generation with Localized Style Representations and Factorization
Few-shot Font Generation with Localized Style Representations and Factorization
few-shot font generation
- generate a new font library (> 20,000 characters) with few references
- without additional training procedures
goal
- capture “font style” from very few glyph images
- font styles are both global, but very local
- character content is also very complex and sensitive to local change!
previous works
- reference images → style, source image → features, merge to generate new image’
- for font, single representation is insufficient to represent “style”
proposed method
- compositionality
- decompose characters into a number of components
- 24 of top 30 used languages has compositionality
- define “multiple localized” style representation using compositionality
- capture multiple local features from each image
- requires guidance
- assign most likely components to each expert
- predict probability by passing multiple local features to component classifier
- match components to express to maximize summation over prediction probability
- style features are trained to
- correctly classify style labels predicted by style classifier
- fooling component labels predicted by component classifier
- maximize independence using HSIC as loss