Semantic-Structural Alignment for Generative Pictorial Charts
Traditional statistical graphics are precise but often lack the visual appeal, memorability, and engagement of pictorial charts. We present a generative framework for the automated synthesis of pictorial charts that bridges the gap between semantic expression and structural faithfulness. Rather than treating charts merely as images to be stylized, we frame the problem as a dual-conditioned generation task guided by two parallel external control signals: a text prompt capturing the semantic context of the editing intent, and a context image providing the abstract statistical chart's global structure. To reinforce these controls within a Multi-Modal Diffusion Transformer, we introduce two c...
| Authors | Zhida Sun, Yulin Zhang, Zheng Gu et al. |
|---|---|
| Primary category | cs.GR |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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