OmniSVG: A Unified Scalable Vector Graphics Generation Model
Scalable Vector Graphics (SVG) is an important image format widely adopted in
graphic design because of their resolution independence and editability. The
study of generating high-quality SVG has continuously drawn attention from both
designers and researchers in the AIGC community. However, existing methods
either produces unstructured outputs with huge computational cost or is limited
to generating monochrome icons of over-simplified structures. To produce
high-quality and complex SVG, we propose OmniSVG, a unified framework that
leverages pre-trained Vision-Language Models (VLMs) for end-to-end multimodal
SVG generation. By parameterizing SVG commands and coordinates into discrete
tokens, OmniSVG decouples structural logic from low-level geometry for
efficient training while maintaining the expressiveness of complex SVG
structure. To further advance the development of SVG synthesis, we introduce
MMSVG-2M, a multimodal dataset with two million richly annotated SVG assets,
along with a standardized evaluation protocol for conditional SVG generation
tasks. Extensive experiments show that OmniSVG outperforms existing methods and
demonstrates its potential for integration into professional SVG design
workflows.
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Yiying Yang
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2025-04-11T12:02:09.692Z
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Yang
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Yiying12
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Wei Cheng
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Wei Cheng
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wchengad
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Sijin Chen
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2025-04-09T07:34:48.985Z
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sijin
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CH3COOK
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Xianfang Zeng
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Jiaxu Zhang
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Liao Wang
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Gang Yu
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Gang Yu
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skicy
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Xingjun Ma
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2025-04-13T19:26:46.463Z
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OpenTAI
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xingjunm
67f5e3701b29460f6a08795c
Yu-Gang Jiang
OmniSVG: A Unified Scalable Vector Graphics Generation Model
Scalable Vector Graphics (SVG) is an important image format widely adopted in
graphic design because of their resolution independence and editability. The
study of generating high-quality SVG has continuously drawn attention from both
designers and researchers in the AIGC community. However, existing methods
either produces unstructured outputs with huge computational cost or is limited
to generating monochrome icons of over-simplified structures. To produce
high-quality and complex SVG, we propose OmniSVG, a unified framework that
leverages pre-trained Vision-Language Models (VLMs) for end-to-end multimodal
SVG generation. By parameterizing SVG commands and coordinates into discrete
tokens, OmniSVG decouples structural logic from low-level geometry for
efficient training while maintaining the expressiveness of complex SVG
structure. To further advance the development of SVG synthesis, we introduce
MMSVG-2M, a multimodal dataset with two million richly annotated SVG assets,
along with a standardized evaluation protocol for conditional SVG generation
tasks. Extensive experiments show that OmniSVG outperforms existing methods and
demonstrates its potential for integration into professional SVG design
workflows.
OmniSVG, using pre-trained Vision-Language Models and discrete tokenization, generates high-quality SVGs efficiently with a large, multimodal dataset.
pre-trained Vision-Language Models VLMs end-to-end multimodal SVG generation discrete tokens scalable vector graphics SVG multimodal dataset MMSVG-2M standardized evaluation protocol conditional SVG generation tasks