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.
683fb0a7be8421eda3152283
Siqi Chen
claimed_verified
2025-06-05T07:28:25.792Z
676b86e79ff0244316f7202f
/avatars/3e1d26312a96752356895ab88eeb3ce0.svg
chensiqi
user
xiaoooobai
683fb0a7be8421eda3152284
Xinyu Dong
683fb0a7be8421eda3152285
Haolei Xu
claimed_verified
2025-06-04T08:56:42.705Z
6692aff88db712bad780f02a
/avatars/5dc4b1c27c70f6a64864711dbff4910f.svg
xhl
user
zjuxhl
683fb0a7be8421eda3152286
Xingyu Wu
claimed_verified
2025-07-25T13:36:37.560Z
674d103d2d93658f850151a6
/avatars/e221d43d3d8601d94cb6a44610054e23.svg
AXy1527
user
wuxingyu
683fb0a7be8421eda3152287
Fei Tang
683fb0a7be8421eda3152288
Hang Zhang
683fb0a7be8421eda3152289
Yuchen Yan
claimed_verified
2025-06-04T08:56:47.244Z
64098738342c26884c792c93
https://cdn-avatars.huggingface.co/v1/production/uploads/64098738342c26884c792c93/SxBUd-wLrl-PjQsrVYJte.jpeg
Yuchen Yan
user
yanyc
683fb0a7be8421eda315228a
Linjuan Wu
683fb0a7be8421eda315228b
Wenqi Zhang
683fb0a7be8421eda315228c
Guiyang Hou
683fb0a7be8421eda315228d
Yongliang Shen
claimed_verified
2025-07-22T07:54:31.566Z
5e1058e9fcf41d740b69966d
/avatars/ce74839ba871f2b54313a670a233ba82.svg
Yongliang Shen
user
tricktreat
683fb0a7be8421eda315228e
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