ICCV 2025 | 扩散模型生成手写体文本行的首次实战，效果惊艳还开源

Core Insights - The article introduces a new generative model called Diffusion Brush, which applies diffusion models to generate realistic handwritten text lines in multiple languages, achieving high fidelity in style, content accuracy, and natural layout [2][4][6]. Research Background - The advancement of handwriting imitation technology has reached a point where AI can accurately replicate an individual's handwriting style, leading to potential applications in font design and handwriting verification [4][6]. - Previous models focused on character-level generation, which often resulted in misalignment and unnatural spacing when assembling text lines [6][7]. Key Issues - The researchers identified two main challenges in handwritten text line generation: ensuring that generated text aligns with human writing habits and maintaining both style fidelity and content readability [16][17]. Technical Solutions - The Diffusion Brush model decouples style and content learning to avoid interference, allowing for more accurate style extraction while ensuring content accuracy [11][12]. - The model employs a multi-scale discriminator to provide detailed content supervision at both line and character levels, balancing global and local accuracy [14][19]. Method Framework - The Diffusion Brush framework includes a style module for content decoupling, a style-content fusion module, a conditional diffusion generator, and a multi-scale content discriminator [13][20]. - The style module uses column and row masking strategies to enhance style learning while preserving essential character information [17][30]. Experimental Evaluation - Quantitative assessments show that Diffusion Brush outperforms existing methods in both English and Chinese datasets, achieving significant improvements in various performance metrics [22][23]. - Qualitative evaluations indicate that Diffusion Brush generates text lines that closely resemble reference samples in terms of character slant, ink depth, and stroke width [24][25]. Summary and Outlook - Diffusion Brush represents a significant advancement in generating personalized handwritten text, with potential applications in custom font creation, historical handwriting restoration, and robust text line recognition training [35].