StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
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Abstract (arXiv)
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model's ability to capture the true semantic representation of visual scenes. This paper posits that an alternative representation of images, vector graphics, can effectively surmount this limitation by enabling a more natural and semantically coherent segmentation of the image information. Thus, we introduce StrokeNUWA, a pioneering work exploring a better visual representation ''stroke tokens'' on vector graphics, which is inherently visual semantics rich, naturally compatible with LLMs, and highly compressed. Equipped with stroke tokens, StrokeNUWA can significantly surpass traditional LLM-based and optimization-based methods across various metrics in the vector graphic generation task. Besides, StrokeNUWA achieves up to a 94x speedup in inference over the speed of prior methods with an exceptional SVG code compression ratio of 6.9%.
Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 32 of its 37 references that are in the map. First posted on arXiv 30 Jan 2024.
Builds on
32 of 37 references.
References · 32
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Citing papers per year: 2024: 8; 2025: 10; 2026: 8.
26 papers.
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- 2024Empowering LLMs to Understand and Generate Complex Vector Graphicscited by 63
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- 2026mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval18 Apr 2026
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- 2024Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey16 Dec 2024
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- 2024SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generation11 Dec 2024
- 2024SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation26 Nov 2024
- 2024Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models25 Nov 2024
- 2024Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision8 Oct 2024
- 2024SVGEditBench: A Benchmark Dataset for Quantitative Assessment of LLM's SVG Editing Capabilities21 Apr 2024
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- 2025SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generationcited by 16
- 2026Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedbackcited by 12
- 2025Discrete Tokenization for Multimodal LLMs: A Comprehensive Surveycited by 7
- 2025DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidancecited by 6
- 2025Bridging Vision, Language, and Mathematics: Pictographic Character Reconstruction with B'ezier Curvescited by 0
- 2026Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback22 Apr 2026
- 2025DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance11 Dec 2025
- 2025Bridging Vision, Language, and Mathematics: Pictographic Character Reconstruction with B'ezier Curves29 Oct 2025
- 2025Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey21 Jul 2025
- 2025SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation3 Jun 2025
- 2025LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformercited by 43
- 2024Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Modelscited by 36
- 2025Style Customization of Text-to-Vector Generation with Image Diffusion Priorscited by 2
- 2024SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generationcited by 1
- 2025Style Customization of Text-to-Vector Generation with Image Diffusion Priors15 May 2025
- 2025LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer3 Feb 2025
- 2024SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generation11 Dec 2024
- 2024Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models25 Nov 2024
- 2025OmniSVG: A Unified Scalable Vector Graphics Generation Modelcited by 71
- 2024Empowering LLMs to Understand and Generate Complex Vector Graphicscited by 63
- 2024Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Surveycited by 27
- 2024SVGEditBench: A Benchmark Dataset for Quantitative Assessment of LLM's SVG Editing Capabilitiescited by 25
- 2025Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawingscited by 9
- 2026VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animationcited by 6
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- 2025Leveraging Large Language Models For Scalable Vector Graphics Processing: A Reviewcited by 3
- 2025AutoSketch: VLM-assisted Style-Aware Vector Sketch Completioncited by 2
- 2026OmniLottie: Generating Vector Animations via Parameterized Lottie Tokenscited by 2
- 2026Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modelingcited by 1
- 2026mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrievalcited by 1
12 most cited of 15 All 15 (JSON)
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