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DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation
[article]
2022
arXiv
pre-print
Preserved Fulltext
In this work, we propose a robust deformable generative network for unsupervised font generation (abbreviated as DGFont++). ...
Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts. ...
., geometric deformation) for the font. Compelled by the above observations, we propose a robust deformable generative model for unsupervised font generation (DGFont++). ...
arXiv:2212.14742v1
fatcat:tchbaqbkerd43jwnm4o6vi4qre
Font Generation with Missing Impression Labels
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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Our goal is to generate fonts with specific impressions, by training a generative adversarial network with a font dataset with impression labels. ...
This paper proposes a font generation model that is robust against missing impression labels. ...
[9] proposed a new deformable DGFont for unsupervised font generation. Vector glyph generation has also been researched. Hayashi et al. ...
arXiv:2203.10348v2
fatcat:i6gvf4xoqfcxrh3qgk7v4oqqbe