Abstract
Generative models, such as diffusion and autoregressive approaches, have demonstrated impressive capabilities in editing natural images. However, applying these tools to scientific charts rests on a flawed assumption: a chart is not merely an arrangement of pixels but a visual representation of structured data governed by a graphical grammar. Consequently, chart editing is not a pixel-manipulation task but a structured transformation problem. To address this fundamental mismatch, we introduce \textit{FigEdit}, a large-scale benchmark for scientific figure editing comprising over 30,000 samples. Grounded in real-world data, our benchmark is distinguished by its diversity, covering 10 distinct chart types and a rich vocabulary of complex editing instructions. The benchmark is organized into five distinct and progressively challenging tasks: single edits, multi edits, conversational edits, visual-guidance-based edits, and style transfer. Our evaluation of a range of state-of-the-art models on this benchmark reveals their poor performance on scientific figures, as they consistently fail to handle the underlying structured transformations required for valid edits. Furthermore, our analysis indicates that traditional evaluation metrics (e.g., SSIM, PSNR) have limitations in capturing the semantic correctness of chart edits. Our benchmark demonstrates the profound limitations of pixel-level manipulation and provides a robust foundation for developing and evaluating future structure-aware models. By releasing \textit{FigEdit} (this https URL), we aim to enable systematic progress in structure-aware figure editing, provide a common ground for fair comparison, and encourage future research on models that understand both the visual and semantic layers of scientific charts.
Abstract (translated)
生成模型，如扩散和自回归方法，在编辑自然图像方面展示了令人印象深刻的性能。然而，将这些工具应用于科学图表时存在一个基本假设上的缺陷：图表不仅仅是像素的排列，而是遵循图形语法的结构化数据的视觉表示形式。因此，图表编辑不是一个像素操作任务，而是一个需要进行结构化转换的问题。为了解决这一根本性的不匹配问题，我们引入了\textit{FigEdit}，这是一个用于科学图表编辑的大规模基准测试集，包含超过30,000个样本。基于真实世界的数据，我们的基准测试以其多样性和涵盖10种不同类型的图表以及丰富复杂的编辑指令词汇表而著称。该基准测试由五项独特且难度递增的任务组成：单一编辑、多任务编辑、对话式编辑、基于视觉指导的编辑和风格转换。 我们对一系列最先进的模型在这一基准上的评估显示，它们在科学图表上表现不佳，因为这些模型始终无法处理有效编辑所需的底层结构化转换。此外，我们的分析表明，传统的评估指标（如SSIM和PSNR）在捕捉图表编辑中的语义正确性方面存在局限性。 \textit{FigEdit}基准测试揭示了基于像素级别的操作所存在的深刻限制，并为开发和评估未来具备结构感知能力的模型提供了坚实的基础。通过发布\textit{FigEdit}(此链接)，我们的目标是促进结构感知图表编辑方面的系统进步，提供一个公平比较的共同基础，并鼓励对理解科学图表视觉和语义层面模型的研究。
URL
https://arxiv.org/abs/2512.00752
