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arXiv · 2609.06116

MM-SVGEdit: A Multimodal-Driven SVG Editing for UI Design

Shibo Yang·Yuqing Gao·Zipeng Liu
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Abstract

In the field of UI design, Scalable Vector Graphics (SVG) is widely used as a design medium. However, traditional SVG editing techniques have high entry barriers and require cumbersome manual iteration, while LLM-based editing solutions suffer from low accuracy and poor user controllability. To address these issues, we propose MM-SVGEdit, a multimodal-driven SVG editing approach that integrates traditional SVG editing and LLM-based methods. We introduce a two-stage strategy in which visual grounding is followed by modification. Both stages support two interaction modalities: natural language instructions and direct manipulation (mouse and keyboard). We trained and evaluated MM-SVGEdit on a self-constructed dataset of 14,476 question-answer pairs generated from UIs, covering 11 types of editing operations on both single and multiple UI targets. The results show that MM-SVGEdit improves SVG editing accuracy, efficiency, and user-perceived control while reducing token consumption and response time.

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Shibo Yang, Yuqing Gao, Zipeng Liu. 2026-09-05. MM-SVGEdit: A Multimodal-Driven SVG Editing for UI Design. https://arxiv.org/abs/2609.06116

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