312 research outputs found
Multi-Content GAN for Few-Shot Font Style Transfer
In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked conditional GAN model considering content along channels and style
along network layers. Our proposed network transfers the style of given glyphs
to the contents of unseen ones, capturing highly stylized fonts found in the
real-world such as those on movie posters or infographics. We seek to transfer
both the typographic stylization (ex. serifs and ears) as well as the textual
stylization (ex. color gradients and effects.) We base our experiments on our
collected data set including 10,000 fonts with different styles and demonstrate
effective generalization from a very small number of observed glyphs
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