Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator

Author:

Kong Yuxin1,Luo Canjie1,Ma Weihong1,Zhu Qiyuan2,Zhu Shenggao2,Yuan Nicholas2,Jin Lianwen1

Affiliation:

1. School of Electronic and Information Engineering, South China University of Technology.

2. Huawei Cloud AI.

Funder

NSFC

Publisher

IEEE

Reference38 articles.

1. SwapText: Image Based Texts Transfer in Scenes

2. DG-Font: Deformable Generative Networks for Unsupervised Font Generation

3. CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generator;wu;cvprw,2020

4. Editing Text in the Wild

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2. An End-to-End Generation Model for Chinese Calligraphy Characters Based on Dense Blocks and Capsule Network;Electronics;2024-07-29

3. QT-Font: High-efficiency Font Synthesis via Quadtree-based Diffusion Models;Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24;2024-07-13

4. SCA-Font: Enhancing Few-Shot Generation with Style-Content Aggregation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. Diff-Font: Diffusion Model for Robust One-Shot Font Generation;International Journal of Computer Vision;2024-06-13

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