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The paper introduces Figma2Code, a new task and dataset for automating design-to-code conversion using multimodal information from Figma files, addressing the limitations of existing image-only approaches. They curated a dataset of 213 high-quality Figma designs with corresponding metadata and code. Benchmarking ten state-of-the-art MLLMs on this dataset reveals that while proprietary models excel in visual fidelity, they struggle with layout responsiveness and code maintainability, often over-relying on primitive visual attributes from Figma metadata.
Proprietary MLLMs generate visually appealing code from Figma designs, but fall short on crucial aspects like responsive layouts and maintainability, revealing a surprising gap in their practical utility.
Front-end development constitutes a substantial portion of software engineering, yet converting design mockups into production-ready User Interface (UI) code remains tedious and costly. While recent work has explored automating this process with Multimodal Large Language Models (MLLMs), existing approaches typically rely solely on design images. As a result, they must infer complex UI details from images alone, often leading to degraded results. In real-world development workflows, however, design mockups are usually delivered as Figma files, a widely used tool for front-end design, that embed rich multimodal information (e.g., metadata and assets) essential for generating high-quality UI. To bridge this gap, we introduce Figma2Code, a new task that advances design-to-code into a multimodal setting and aims to automate design-to-code in the wild. Specifically, we collect paired design images and their corresponding metadata files from the Figma community. We then apply a series of processing operations, including rule-based filtering, human- and MLLM-based annotation and screening, and metadata refinement. This process yields 3,055 samples, from which designers curate a balanced dataset of 213 high-quality cases. Using this dataset, we benchmark ten state-of-the-art open-source and proprietary MLLMs. Our results show that while proprietary models achieve superior visual fidelity, they remain limited in layout responsiveness and code maintainability. Further experiments across modalities and ablation studies corroborate this limitation, partly due to models'tendency to directly map primitive visual attributes from Figma metadata.
