SDT

dailenson/SDT is ranked 5,559 among these 8 peers; OpenMOSS/MOSS-TTSD is ranked 5,563.

🔥 Disentangling Writer and Character Styles for Handwriting Generation 📢 Introduction The proposed style-disentangled Transformer (SDT) generates online handwritings with conditional content and style. Existing RNN-based methods mainly focus on capturing a person’s overall writing style, neglecting subtle style inconsistencies between characters written by the same person. In light of this, SDT disentangles the writer-wise and character-wise style representations from individual handwriting samples for enhancing imitation performance. We extend SDT and introduce an offline-to-offline framework

for improving the generation quality of offline Chinese handwritings. Overview of our SDT Three samples of online characters with writing orders 📅 News [2025/06/26] 🎉🎉🎉 DiffBrush, a novel state-of-the-art approach for full-line text generation, is accepted to ICCV 2025. [2024/11/26] 🎉🎉🎉 Release of the implementations of Content Score and Style Score. [2024/07/01] 🎉🎉🎉 A new state-of-the-art method for handwritten text generation, named One-DM, is accepted by ECCV 2024. [2024/01

/07] Add a tutorial and code for synthesizing handwriting with user-customized styles, more information can be found here. [2023/12/15] 🎉🎉🎉 This work is reported by a top bilibili video blogger with 2. 7 million followers and received nearly one million views. [2023/10/10] The author is invited to give a talk (in Chinese) by CSIG (China Society of Image and Graphics). [2023/04/12] Initial release of the datasets, pre-trained models, training and testing codes. [2023

/02/28] 🎉🎉🎉 Our SDT is accepted by CVPR 2023. 📺 Handwriting generation results **Online Chinese handwriting generation** **Applications to various scripts** **Extension on offline Chinese handwriting generation** 🔨 Requirements 📂 Folder Structure 💿 Datasets We provide Chinese, Japanese and English datasets in Google Drive | Baidu Netdisk PW:xu9u. Please download these datasets, uzip them and move the extracted files to /data. 🚀 Training & Test **Training** To train the SDT on the Chinese dataset, run this command: To train the SDT on the Japanese dataset, run

this command: To train the SDT on the English dataset, run this command: **Qualitative Test** To generate **online Chinese handwritings** with our SDT, run this command: To generate **offline Chinese handwriting images** with our SDT, run this command: To generate **online Japanese handwritings** with our SDT, run this command: To generate **offline Japanese handwriting images** with our SDT, run this command: To generate **online English handwritings** with our SDT, run this command: To generate **offline English handwriting images** with our

SDT, run this command: **Quantitative Evaluation** To evaluate the generated handwritings, you need to set to the path of the generated handwritings (e. , Generated/Chinese), and run this command: To calculate the Content Score of generated handwritings, you need to set to the path of the generated handwritings (e. , Generated/Chinese), and run this command: To calculate the Style Score of generated handwritings, you need to set to the path of the generated handwriting images (e. , Generated_img/Chinese), and

run this command: 🏰 Practical Application We are delighted to discover that **P0etry-rain** has proposed a pipeline that involves initially