InternSVG: Towards Unified SVG Tasks with Multimodal Large Language
  Models
General SVG modeling remains challenging due to fragmented datasets, limited
transferability of methods across tasks, and the difficulty of handling
structural complexity. In response, we leverage the strong transfer and
generalization capabilities of multimodal large language models (MLLMs) to
achieve unified modeling for SVG understanding, editing, and generation. We
present the InternSVG family, an integrated data-benchmark-model suite. At its
core is SAgoge, the largest and most comprehensive multimodal dataset for SVG
tasks, encompassing both static graphics and dynamic animations. It covers
icons, long-sequence illustrations, scientific diagrams, and dynamic
animations, supporting tasks of varied difficulty levels and providing deeper
hierarchies with richer attributes compared to previous datasets. Based on this
resource, we introduce SArena, a companion benchmark with comprehensive task
definitions and standardized evaluation that aligns with the domains and
difficulty spectrum covered by SAgoge. Building on these foundations, we
propose InternSVG, a unified MLLM for SVG understanding, editing, and
generation with SVG-specific special tokens, subword-based embedding
initialization, and a two-stage training strategy that progresses from short
static SVGs to long-sequence illustrations and complex animations. This unified
formulation induces positive transfer and improves overall performance.
Experiments on SArena and prior benchmark confirm that InternSVG achieves
substantial gains and consistently outperforms leading open and proprietary
counterparts.
68edbe0fde1fee572713a858
Haomin Wang
claimed_verified
2025-10-15T07:07:54.404Z
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Haomin Wang
user
KiyotakaWang
68edbe0fde1fee572713a859
Jinhui Yin
68edbe0fde1fee572713a85a
Qi Wei
claimed_verified
2025-10-15T15:25:16.189Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6800b9e1cc408935c6abc857/ABB1DCn6VS03ohrbvAs_o.jpeg
Qi Wei
user
wzq57
68edbe0fde1fee572713a85b
Wenguang Zeng
68edbe0fde1fee572713a85c
Lixin Gu
68edbe0fde1fee572713a85d
Shenglong Ye
68edbe0fde1fee572713a85e
Zhangwei Gao
68edbe0fde1fee572713a85f
Yaohui Wang
68edbe0fde1fee572713a860
Yanting Zhang
68edbe0fde1fee572713a861
Yuanqi Li
68edbe0fde1fee572713a862
Yanwen Guo
68edbe0fde1fee572713a863
Wenhai Wang
68edbe0fde1fee572713a864
Kai Chen
68edbe0fde1fee572713a865
Yu Qiao
68edbe0fde1fee572713a866
Hongjie Zhang
InternSVG: Towards Unified SVG Tasks with Multimodal Large Language
  Models
General SVG modeling remains challenging due to fragmented datasets, limited
transferability of methods across tasks, and the difficulty of handling
structural complexity. In response, we leverage the strong transfer and
generalization capabilities of multimodal large language models (MLLMs) to
achieve unified modeling for SVG understanding, editing, and generation. We
present the InternSVG family, an integrated data-benchmark-model suite. At its
core is SAgoge, the largest and most comprehensive multimodal dataset for SVG
tasks, encompassing both static graphics and dynamic animations. It covers
icons, long-sequence illustrations, scientific diagrams, and dynamic
animations, supporting tasks of varied difficulty levels and providing deeper
hierarchies with richer attributes compared to previous datasets. Based on this
resource, we introduce SArena, a companion benchmark with comprehensive task
definitions and standardized evaluation that aligns with the domains and
difficulty spectrum covered by SAgoge. Building on these foundations, we
propose InternSVG, a unified MLLM for SVG understanding, editing, and
generation with SVG-specific special tokens, subword-based embedding
initialization, and a two-stage training strategy that progresses from short
static SVGs to long-sequence illustrations and complex animations. This unified
formulation induces positive transfer and improves overall performance.
Experiments on SArena and prior benchmark confirm that InternSVG achieves
substantial gains and consistently outperforms leading open and proprietary
counterparts.
A unified multimodal large language model (MLLM) for SVG understanding, editing, and generation leverages a comprehensive dataset and benchmark to achieve superior performance across various tasks.
multimodal large language models MLLMs SVG understanding SVG editing SVG generation SAgoge SArena SVG-specific special tokens subword-based embedding initialization two-stage training strategy