InkFlow: Beyond Style Transfer via Writing Flow Learning for Online Handwriting Generation

Stylized handwriting generation aims to synthesize new content with specific writer characteristics from reference handwriting samples. Existing methods typically follow the style transfer paradigm, treating style features disentangled from reference samples as the transferable object and fusing them with target text to generate the final result. This paradigm focuses on the ”style presented in the outcome.” In contrast, we focus on ”how such outcomes are produced”: a specific handwriting result can be viewed as a manifestation of how a writer transforms textual content into trajectories under a given content condition. Accordingly, we redefine the transferable object from static style features to the underlying content-to-trajectory transformation, which we term the $\text{writing flow}$, and transfer it to new content generation. Based on this perspective, we propose $\text{InkFlow}$, a writing-flow learning framework for online handwriting generation. Given a complete reference text–trajectory example, InkFlow models the underlying content-to-trajectory relationship and transfers it to new target text. Specifically, we adopt a latent diffusion framework with a task-aware VAE that learns a structured handwriting latent space with both content- and style-aware properties, and design a reference–target conditioned Diffusion Transformer to implicitly model and transfer writing flows. Furthermore, we introduce a writing-flow alignment objective to enhance the preservation of reference writing characteristics in the generated results. Extensive experiments on the IAM-OnDB and ICDAR-2013 text-line handwriting benchmarks demonstrate that our model achieves strong performance in both content fidelity and writer-style consistency. Code will be publicly available.