DiagramGen

[📑paper link](https://arxiv.org/abs/2411.11916) ## Dataset Card: DiagramAgent/DiagramGenBenchmark ### 1. Overview **DiagramAgent/DiagramGenBenchmark** is a comprehensive benchmark designed for evaluating text-to-diagram generation and editing tasks. It provides a diverse set of diagram types alongside corresponding textual descriptions and code representations, aiming to facilitate research in generating structured visual content from natural language inputs. ### 2. Dataset Description - **Objective**: To transform textual instructions into structured, logically coherent diagrams. - **Content**: The dataset includes a wide range of diagram types: - **Model Architecture Diagrams** - **Flowcharts** - **Line Charts** - **Directed Graphs** - **Undirected Graphs** - **Tables** - **Bar Charts** - **Mind Maps** - **Data Format**: Each sample typically contains: - A user instruction or query describing the diagram. - The corresponding diagram code (written primarily in LaTeX or DOT) that can be compiled into a visual diagram. ### 3. Data Sources - The dataset aggregates samples from multiple public resources: - HuggingFace’s VGQA dataset - Datikz and Datikz-v2 datasets - Open-source repositories on GitHub and Overleaf - **Licensing**: The sources are licensed under CC BY 4.0 or MIT, ensuring open access while respecting original content rights. ### 4. Citation If you find our work helpful, feel free to give us a cite. ``` @inproceedings{wei2024wordsstructuredvisualsbenchmark, title={From Words to Structured Visuals: A Benchmark and Framework for Text-to-Diagram Generation and Editing}, author={Jingxuan Wei and Cheng Tan and Qi Chen and Gaowei Wu and Siyuan Li and Zhangyang Gao and Linzhuang Sun and Bihui Yu and Ruifeng Guo}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year={2025} } ```