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• Existing text-to-SVG generation methods are limited because their autoregressive training does not incorporate visual perception of the final rendered image.limitation 90%
• IntroSVG achieves state-of-the-art performance across several key evaluation metrics.result 90%
• IntroSVG generates SVGs with more complex structures, stronger semantic alignment, and greater editability than previous methods.result 85%
• The framework uses a unified VLM that operates in a closed loop, assuming dual roles of both generator and critic.method 90%
• The method systematically converts early-stage failures into high-quality error-correction training data to enhance model robustness.method 85%
• The method uses a high-capacity teacher VLM to construct a preference dataset and aligns the generator's policy through Direct Preference Optimization (DPO).method 85%
• During inference, the optimized generator and critic operate collaboratively in an iterative 'generate-review-refine' cycle.method 90%
• Incorporating explicit visual feedback into the generation loop is effective for improving SVG generation quality.technical 85%

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