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DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

arXiv 2007.11301 ↗First posted 22 Jul 2020cs.CVcited by 158

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Abstract (arXiv)

Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based models applied to rasterized images, the problem of vector graphics representation learning and generation remains largely unexplored. In this work, we propose a novel hierarchical generative network, called DeepSVG, for complex SVG icons generation and interpolation. Our architecture effectively disentangles high-level shapes from the low-level commands that encode the shape itself. The network directly predicts a set of shapes in a non-autoregressive fashion. We introduce the task of complex SVG icons generation by releasing a new large-scale dataset along with an open-source library for SVG manipulation. We demonstrate that our network learns to accurately reconstruct diverse vector graphics, and can serve as a powerful animation tool by performing interpolations and other latent space operations. Our code is available at https://github.com/alexandre01/deepsvg.

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 11; 2023: 24; 2024: 29; 2025: 35; 2026: 42.

158 papers.

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  1. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  2. 2022VectorMapNet: End-to-end Vectorized HD Map Learningcited by 243
  3. 2021Im2Vec: Synthesizing Vector Graphics without Vector Supervisioncited by 114
  4. 2023StarVector: Generating Scalable Vector Graphics Code from Images and Textcited by 96
  5. 2024SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Modelcited by 95
  6. 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 93
  7. 2021Constrained Graphic Layout Generation via Latent Optimizationcited by 83
  8. 2021Computer-Aided Design as Languagecited by 82
  9. 2022Towards Layer-wise Image Vectorizationcited by 77
  10. 2023SVGDreamer: Text Guided SVG Generation with Diffusion Modelcited by 75
  11. 2023AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZcited by 74
  12. 2023IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformerscited by 73
38 more
  1. 2025OmniSVG: A Unified Scalable Vector Graphics Generation Modelcited by 71
  2. 2021SketchGen: Generating Constrained CAD Sketchescited by 68
  3. 2021Vitruvion: A Generative Model of Parametric CAD Sketchescited by 66
  4. 2024Empowering LLMs to Understand and Generate Complex Vector Graphicscited by 63
  5. 2021Engineering Sketch Generation for Computer-Aided Designcited by 49
  6. 2025LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformercited by 43
  7. 2024Graphic Design with Large Multimodal Modelcited by 38
  8. 2024Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Modelscited by 36
  9. 2022CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphicscited by 34
  10. 2024Text-to-Vector Generation with Neural Path Representationcited by 32
  11. 2023Towards Flexible Multi-modal Document Modelscited by 30
  12. 2024SketchAgent: Language-Driven Sequential Sketch Generationcited by 30
  13. 2024TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Cloudscited by 29
  14. 2023DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Qualitycited by 27
  15. 2024StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesiscited by 26
  16. 2021De-rendering Stylized Textscited by 24
  17. 2023PLay: Parametrically Conditioned Layout Generation using Latent Diffusioncited by 24
  18. 2023ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitivescited by 22
  19. 2021A Multi-Implicit Neural Representation for Fontscited by 21
  20. 2023VecFusion: Vector Font Generation with Diffusioncited by 20
  21. 2025LayerD: Decomposing Raster Graphic Designs into Layerscited by 20
  22. 2021Multiset-Equivariant Set Prediction with Approximate Implicit Differentiationcited by 17
  23. 2024AniClipart: Clipart Animation with Text-to-Video Priorscited by 17
  24. 2024NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generationcited by 17
  25. 2024Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesiscited by 17
  26. 2021Cloud2Curve: Generation and Vectorization of Parametric Sketchescited by 16
  27. 2023DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representationcited by 16
  28. 2025NeuralSVG: An Implicit Representation for Text-to-Vector Generationcited by 16
  29. 2025SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generationcited by 16
  30. 2022LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designercited by 15
  31. 2021DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learningcited by 14
  32. 2023ChiroDiff: Modelling chirographic data with Diffusion Modelscited by 14
  33. 2024SVGBuilder: Component-Based Colored SVG Generation with Text-Guided Autoregressive Transformerscited by 14
  34. 2025UniSVG: A Unified Dataset for Vector Graphic Understanding and Generation with Multimodal Large Language Modelscited by 14
  35. 2024SketchINR: A First Look into Sketches as Implicit Neural Representationscited by 13
  36. 2025SVGen: Interpretable Vector Graphics Generation with Large Language Modelscited by 13
  37. 2025InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Modelscited by 12
  38. 2026Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedbackcited by 12

50 most cited of 158 All 158 (JSON)