SE-GAN: Skeleton Enhanced Gan-Based Model for Brush Handwriting Font Generation

Author:

Yuan Shaozu1,Liu Ruixue1,Chen Meng1,Chen Baoyang2,Qiu Zhijie2,He Xiaodong1

Affiliation:

1. JD AI,Beijing,China

2. Central Academy of Fine Arts,China

Publisher

IEEE

Reference23 articles.

1. U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation;kim;International Conference on Learning Representations,0

2. Separating Style and Content for Generalized Style Transfer

3. Dg-font: Deformable generative networks for unsupervised font generation;li;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,0

4. Learning to Compose Stylistic Calligraphy Artwork with Emotions

5. Generating handwritten chinese characters using cycle-gan;bo;2018 IEEE Winter Conference on Applications of Computer Vision (WACV),0

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1. Review of GAN-Based Research on Chinese Character Font Generation;Chinese Journal of Electronics;2024-05

2. CLIP-Font: Sementic Self-Supervised Few-Shot Font Generation with Clip;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

3. Chinese Character Font Generation Based on Diffusion Model;2024 5th International Conference on Computer Engineering and Application (ICCEA);2024-04-12

4. Unsupervised image-to-image translation with multiscale attention generative adversarial network;Applied Intelligence;2024-04

5. Glyph Generation for Japanese Calligraphy based on Encoding both Content and Style;2024 IEEE International Conference on Big Data and Smart Computing (BigComp);2024-02-18

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