title: OmniSVG: A Unified Scalable Vector Graphics Generation Model
summary: 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.
paper_authors._id: 67f5e3701b29460f6a087954
paper_authors.name: Yiying Yang
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paper_authors.fullname: Yang
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paper_authors.user: Yiying12
paper_authors._id: 67f5e3701b29460f6a087955
paper_authors.name: Wei Cheng
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paper_authors.fullname: Wei Cheng
paper_authors.type: user
paper_authors.user: wchengad
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paper_authors.name: Sijin Chen
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paper_authors.fullname: sijin
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paper_authors.user: CH3COOK
paper_authors._id: 67f5e3701b29460f6a087957
paper_authors.name: Xianfang Zeng
paper_authors._id: 67f5e3701b29460f6a087958
paper_authors.name: Jiaxu Zhang
paper_authors._id: 67f5e3701b29460f6a087959
paper_authors.name: Liao Wang
paper_authors._id: 67f5e3701b29460f6a08795a
paper_authors.name: Gang Yu
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paper_authors.fullname: Gang Yu
paper_authors.type: user
paper_authors.user: skicy
paper_authors._id: 67f5e3701b29460f6a08795b
paper_authors.name: Xingjun Ma
paper_authors.status: claimed_verified
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paper_authors._id: 64cb54bcbb5d195b99186e15
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paper_authors.fullname: OpenTAI
paper_authors.type: user
paper_authors.user: xingjunm
paper_authors._id: 67f5e3701b29460f6a08795c
paper_authors.name: Yu-Gang Jiang
paper_title: OmniSVG: A Unified Scalable Vector Graphics Generation Model
paper_summary: 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.
paper_ai_summary: OmniSVG, using pre-trained Vision-Language Models and discrete tokenization, generates high-quality SVGs efficiently with a large, multimodal dataset.
paper_ai_keywords: 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