用文字图像数值联合引导生成可交互SVG，支持多种创作场景。
RoboSVG: A Unified Framework for Interactive SVG Generation with Multi-modal Guidance
- 融合文本、图像、数值信号生成交互式SVG
- 在百万级数据集上实现高保真度与查询符合率
- 适合设计自动化、机器人路径生成等应用
可缩放矢量图形（SVG）是数字设计和机器人控制的基础，不仅编码视觉结构，还包含交互绘制中的运动路径。本文提出RoboSVG，一个统一的多模态框架，通过文本、视觉和数值信号生成交互式SVG。给定输入查询后，模型先生成多模态引导，再通过专用生成模块合成候选SVG，最后在数值引导下进行精细化优化，产出高质量结果。为支持该框架，我们构建了包含一百万条样本的RoboDraw数据集，每条数据对应一个生成条件（如文本、图像、部分SVG）及其真实SVG代码。该数据集支持对四项任务的系统研究：基础生成（文本到SVG、图像到SVG）和交互生成（部分SVG到SVG、部分图像到SVG）。大量实验表明，RoboSVG在各项任务中均实现了卓越的查询符合率与视觉保真度，建立了多功能SVG生成的新基准。项目数据集与源代码即将公开。
原文摘要 · Abstract (English)
Scalable Vector Graphics (SVGs) are fundamental to digital design and robot control, encoding not only visual structure but also motion paths in interactive drawings. In this work, we introduce RoboSVG, a unified multimodal framework for generating interactive SVGs guided by textual, visual, and numerical signals. Given an input query, the RoboSVG model first produces multimodal guidance, then synthesizes candidate SVGs through dedicated generation modules, and finally refines them under numerical guidance to yield high-quality outputs. To support this framework, we construct RoboDraw, a large-scale dataset of one million examples, each pairing an SVG generation condition (e.g., text, image, and partial SVG) with its corresponding ground-truth SVG code. RoboDraw dataset enables systematic study of four tasks, including basic generation (Text-to-SVG, Image-to-SVG) and interactive generation (PartialSVG-to-SVG, PartialImage-to-SVG). Extensive experiments demonstrate that RoboSVG achieves superior query compliance and visual fidelity across tasks, establishing a new state of the art in versatile SVG generation. The dataset and source code of this project will be publicly available soon.
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