SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Large Language Models (LLMs) and Multimodal LLMs have shown promising
capabilities for SVG processing, yet existing benchmarks suffer from limited
real-world coverage, lack of complexity stratification, and fragmented
evaluation paradigms. We introduce SVGenius, a comprehensive benchmark
comprising 2,377 queries across three progressive dimensions: understanding,
editing, and generation. Built on real-world data from 24 application domains
with systematic complexity stratification, SVGenius evaluates models through 8
task categories and 18 metrics. We assess 22 mainstream models spanning
different scales, architectures, training paradigms, and accessibility levels.
Our analysis reveals that while proprietary models significantly outperform
open-source counterparts, all models exhibit systematic performance degradation
with increasing complexity, indicating fundamental limitations in current
approaches; however, reasoning-enhanced training proves more effective than
pure scaling for overcoming these limitations, though style transfer remains
the most challenging capability across all model types. SVGenius establishes
the first systematic evaluation framework for SVG processing, providing crucial
insights for developing more capable vector graphics models and advancing
automated graphic design applications. Appendix and supplementary materials
(including all data and code) are available at
https://zju-real.github.io/SVGenius.
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Siqi Chen
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2025-06-05T07:28:25.792Z
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chensiqi
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xiaoooobai
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Xinyu Dong
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Haolei Xu
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2025-06-04T08:56:42.705Z
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Xingyu Wu
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2025-07-25T13:36:37.560Z
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wuxingyu
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wuxingyu
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Fei Tang
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Hang Zhang
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Yuchen Yan
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2025-06-04T08:56:47.244Z
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Yuchen Yan
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Linjuan Wu
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Wenqi Zhang
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Guiyang Hou
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Yongliang Shen
claimed_verified
2025-07-22T07:54:31.566Z
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Yongliang Shen
user
tricktreat
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Weiming Lu
683fb0a7be8421eda315228f
Yueting Zhuang
SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Large Language Models (LLMs) and Multimodal LLMs have shown promising
capabilities for SVG processing, yet existing benchmarks suffer from limited
real-world coverage, lack of complexity stratification, and fragmented
evaluation paradigms. We introduce SVGenius, a comprehensive benchmark
comprising 2,377 queries across three progressive dimensions: understanding,
editing, and generation. Built on real-world data from 24 application domains
with systematic complexity stratification, SVGenius evaluates models through 8
task categories and 18 metrics. We assess 22 mainstream models spanning
different scales, architectures, training paradigms, and accessibility levels.
Our analysis reveals that while proprietary models significantly outperform
open-source counterparts, all models exhibit systematic performance degradation
with increasing complexity, indicating fundamental limitations in current
approaches; however, reasoning-enhanced training proves more effective than
pure scaling for overcoming these limitations, though style transfer remains
the most challenging capability across all model types. SVGenius establishes
the first systematic evaluation framework for SVG processing, providing crucial
insights for developing more capable vector graphics models and advancing
automated graphic design applications. Appendix and supplementary materials
(including all data and code) are available at
https://zju-real.github.io/SVGenius.
SVGenius evaluates Large Language Models and Multimodal LLMs for SVG processing using a comprehensive benchmark across three dimensions: understanding, editing, and generation, revealing insights into model capabilities and limitations.
Large Language Models Multimodal LLMs SVG processing SVGenius complexity stratification reasoning-enhanced training style transfer