Fig. 1
From: CalliFormer: a structure-aware transformer for Chinese calligraphy generation
Architecture of the Calliformer model. The proposed Calliformer employs an encoder-decoder framework (U-Net Generator) for calligraphy image synthesis. It integrates dedicated branches: a Graph Transformer-based Structure Encoder (Es, c) to process character component and structural information derived from the CCTS-2025 dataset indexed by the Unicode, and a Content-aware Style Encoder (Estyle) to extract artistic style from a reference calligraphy image. An internal image encoder (Ei) within the U-Net processes a standard character image for content. These structural (vstruct), style (vstyle), and content (vi) features are fused to guide the generation process. A discriminator provides adversarial feedback and performs style analysis (regression and classification). Key training objectives indicated include component and structure losses, pixel-wise reconstruction loss, adversarial loss, and style regression loss.
