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IntroSVG introduces a novel framework for text-to-SVG generation that incorporates visual perception by using a unified VLM as both generator and critic in a closed loop. The model is first Supervised Fine-Tuned (SFT) to generate SVGs and provide feedback on rendered outputs, with early-stage failures converted into error-correction training data. Then, Direct Preference Optimization (DPO) is used to align the generator's policy based on a preference dataset constructed by a teacher VLM, enabling iterative refinement during inference and achieving state-of-the-art performance.
By having a single VLM critique its own SVG renderings, IntroSVG learns to generate more complex, semantically aligned, and editable vector graphics from text prompts.
Scalable Vector Graphics (SVG) are central to digital design due to their inherent scalability and editability. Despite significant advancements in content generation enabled by Visual Language Models (VLMs), existing text-to-SVG generation methods are limited by a core challenge: the autoregressive training process does not incorporate visual perception of the final rendered image, which fundamentally constrains generation quality. To address this limitation, we propose an Introspective SVG Generation Framework (IntroSVG). At its core, the framework instantiates a unified VLM that operates in a closed loop, assuming dual roles of both generator and critic. Specifically, through Supervised Fine-Tuning (SFT), the model learns to draft SVGs and to provide feedback on their rendered outputs; moreover, we systematically convert early-stage failures into high-quality error-correction training data, thereby enhancing model robustness. Subsequently, we leverage a high-capacity teacher VLM to construct a preference dataset and further align the generator's policy through Direct Preference Optimization (DPO). During inference, the optimized generator and critic operate collaboratively in an iterative"generate-review-refine"cycle, starting from imperfect intermediate drafts to autonomously improve output quality. Experimental results demonstrate that our method achieves state-of-the-art performance across several key evaluation metrics, generating SVGs with more complex structures, stronger semantic alignment, and greater editability. These results corroborate the effectiveness of incorporating explicit visual feedback into the generation loop.
