Many expressive visualizations are shared online only as bitmap images, making them difficult to redesign or adapt to new data. Reusing such image-based visualizations requires substantial expertise and is often time-consuming, even for experienced visualization practitioners. Existing work on reproducing visualizations often relies on structured SVG or specifications, supports limited visualization types, and offers limited flexibility for customization. To address these challenges, we present ReVis, a human-AI collaboration approach that enables flexible reuse of image-based visualizations. First, a generic Domain-Specific language (DSL) is proposed to model complex visualizations and support both visualization decomposition and reproduction. Then, ReVis employs an MLLM-based pipeline to parse an image-based visualization into the DSL, delineating its core visual structures and data-to-encoding mappings, and further reproduces the visualization from the DSL. Finally, ReVis includes an interactive interface to allow users to upload visualization images, inspect reproduced results, update the underlying data, and customize visual encodings. A gallery of 40 visualizations demonstrates the expressiveness of the DSL, and a quantitative study evaluates the reproduction quality of ReVis on these examples. Two usage scenarios and user interviews with 16 visualization practitioners demonstrate the effectiveness of ReVis.

翻译：许多富有表现力的可视化作品仅以位图图像形式在网络上分享，这使得它们难以被重新设计或适配新数据。复用这类基于图像的可视化需要具备大量专业知识，且即使对于经验丰富的可视化从业者而言，往往也十分耗时。现有的可视化复现工作通常依赖于结构化的SVG或规范描述，支持的可视化类型有限，并且对自定义化的灵活性支持不足。为解决这些挑战，我们提出了ReVis，一种人机协作的方法，能够灵活地复用基于图像的可视化。首先，我们提出了一种通用的领域特定语言（DSL），用于对复杂可视化进行建模，并支持可视化的分解与复现。随后，ReVis采用基于多模态大语言模型（MLLM）的流水线，将基于图像的可视化解析为DSL，勾勒出其核心视觉结构以及数据到编码的映射关系，并进一步根据该DSL复现可视化。最后，ReVis包含一个交互式界面，允许用户上传可视化图像、检查复现结果、更新底层数据以及自定义视觉编码。一个包含40个可视化作品的画廊展示了该DSL的表现力，一项定量研究评估了ReVis在这些示例上的复现质量。两个使用场景以及对16位可视化从业者的用户访谈，证明了ReVis的有效性。