Form Generation of Lost Ancient Chinese Characters Using Component Priors by Diffusion Model

The form generation of lost ancient Chinese characters is a meaningful but challenging task. The existing methods for form generation of lost ancient Chinese characters do not utilize domain-specific knowledge fully and provide inadequate guidance in the generation process. In this paper, we propose a diffusion-based method to improve the form generation of lost ancient Chinese characters by domain-specific knowledge of components and enhancing guidance in the generation process. In particular, to improve generation quality by component priors, we introduce a component pre-training stage to learn evolutionary patterns of components. To enhance guidance in the generation process, we inject conditional features into the bottleneck of the U-Net to improve the similarity between the generated and target character forms. In addition, to better generate the details of components, we employ a unified patch sampling strategy across stages, ensuring a direct correspondence between local component features. Extensive experiments on the Font-607 dataset show that our method improves the quality of ancient Chinese character generation. For example, our method reduces the LPIPS score from 0.2517 to 0.2346, and increases the cosine similarity from 0.7841 to 0.8094 in the Seal Script generation task. Code is available at https://anonymous.4open.science/r/Anc-Diff-81EA.