One-Shot Diffusion Mimicker for Handwritten Text Generation
Existing handwritten text generation methods often require more than ten handwriting samples as style references. However, in practical applications, users tend to prefer a handwriting generation model that operates with just a single reference sample for its convenience and efficiency. This approac...
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Zusammenfassung: | Existing handwritten text generation methods often require more than ten
handwriting samples as style references. However, in practical applications,
users tend to prefer a handwriting generation model that operates with just a
single reference sample for its convenience and efficiency. This approach,
known as "one-shot generation", significantly simplifies the process but poses
a significant challenge due to the difficulty of accurately capturing a
writer's style from a single sample, especially when extracting fine details
from the characters' edges amidst sparse foreground and undesired background
noise. To address this problem, we propose a One-shot Diffusion Mimicker
(One-DM) to generate handwritten text that can mimic any calligraphic style
with only one reference sample. Inspired by the fact that high-frequency
information of the individual sample often contains distinct style patterns
(e.g., character slant and letter joining), we develop a novel style-enhanced
module to improve the style extraction by incorporating high-frequency
components from a single sample. We then fuse the style features with the text
content as a merged condition for guiding the diffusion model to produce
high-quality handwritten text images. Extensive experiments demonstrate that
our method can successfully generate handwriting scripts with just one sample
reference in multiple languages, even outperforming previous methods using over
ten samples. Our source code is available at
https://github.com/dailenson/One-DM. |
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DOI: | 10.48550/arxiv.2409.04004 |