CalliMaster: Mastering Page-level Chinese Calligraphy via Layout-guided Spatial Planning

Generating a Chinese calligraphy page requires joint control of character placement, writing style, and stroke connections across characters. These aspects are interdependent. A calligrapher’s style affects character size and spacing, while connecting strokes depend on surrounding characters. Current character-level methods ignore this page context, while existing page-level and general-purpose models often struggle to preserve coherent spatial structure without weakening brushwork fidelity. We present CalliMaster, a unified framework for page-level calligraphy generation and editing that follows the practice of "planning before writing". Given target text rendered in a standard font and a target style, CalliMaster first predicts a page layout and then renders calligraphy under that layout, adapting character rendering to the surrounding composition. A single multimodal diffusion transformer performs both layout planning and layout-guided rendering. It represents layouts and calligraphy pages in a shared visual space. The predicted layout can also be edited. Users can move, resize, insert, or delete characters. The model then replans the surrounding space and regenerates connecting strokes. The same framework further supports restoration under simulated damage and exploratory authenticity analysis. Experiments show that CalliMaster improves generation quality over specialized calligraphy and general-purpose baselines and outperforms a two-model variant on image and layout metrics.