Glyph-to-Image: Semantic Chinese Glyph Generation via Text-to-Image Models

Chinese characters combine symbolic identity with highly structured visual forms, making them a natural medium for semantic visual expression. However, automatically transforming prescribed Chinese glyphs into concept-bearing images while preserving their character identity remains challenging, since unconstrained generative models provide strong semantics but limited structural control. We introduce Glyph-to-Image, a task that transforms editable Chinese glyphs according to a target concept. Starting from native cubic B\'ezier outlines, Glyph-to-Image jointly optimizes glyph geometry and a learnable appearance field through differentiable rendering. To stabilize semantic appearance formation, we introduce an Appearance Low-Pass Filter (ALPF) that constrains the bandwidth of both the rendered appearance and its updates. To preserve local glyph structure during semantic deformation, we further introduce Glyph Consistency Regularization (GCR), which regularizes neighboring B\'ezier segments with respect to local similarity transformations. Beyond single-character generation, Glyph-to-Image naturally extends to multi-character generation. We establish benchmarks, develop evaluation metrics, and reproduce representative methods from semantic and artistic typography as baselines. Extensive experiments show that the proposed Glyph-to-Image achieves a better balance between semantic expressiveness and character readability than existing approaches.