ChartCraft: Towards Aesthetic and Context-Aware Chart Generation

Structured data visualizations such as tables and charts are widely used to communicate information, yet existing chart generation datasets often suffer from limited aesthetics and insufficient contextual cues, hindering interpretability. We present ChartCraft, a framework and curated dataset designed to advance aesthetic and context-aware chart generation. Our approach focuses on transforming raw or minimally structured inputs into visually refined and context-enriched tables and charts that improve readability, semantic clarity, and visual coherence. To support this goal, we construct a diverse dataset spanning four visualization types (tables, bar, line, and pie charts), and define six generation tasks with annotations for layout, styling, and contextual augmentation. Building on this resource, we train a model termed ChartCrafter using SFT and RL, jointly modeling visual design and contextual reasoning for chart generation. We further introduce a new evaluation benchmark, CraftEval, to assess both aesthetic quality and contextual awareness. Experimental results demonstrate that models trained with ChartCraft produce charts that are more visually consistent and contextually informative, surpassing the baseline by +26.81 and +24.01 points, respectively on T2I and editing tasks of CraftEval. Our work highlights the importance of integrating design-aware and context-aware principles into aesthetic chart generation.