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DesigNet: Learning to Draw Vector Graphics as Designers Do

arXiv 2604.06494 ↗First posted 7 Apr 2026cs.CV cs.GRcited by 0

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Abstract (arXiv)

AI-driven content generation has made remarkable progress in recent years. However, neural networks and human designers operate in fundamentally different ways, making collaboration between them challenging. We address this gap for Scalable Vector Graphics (SVG) by equipping neural networks with tools commonly used by designers, such as axis alignment and explicit continuity control at command junctions. We introduce DesigNet, a hierarchical Transformer-VAE that operates directly on SVG sequences with a continuous command parameterization. Our main contributions are two differentiable modules: a continuity self-refinement module that predicts C0, G1, and C1 continuity for each curve point and enforces it by modifying B'ezier control points, and an alignment self-refinement module with snapping capabilities for horizontal or vertical lines. DesigNet produces editable outlines and achieves competitive results against state-of-the-art methods, with notably higher accuracy in continuity and alignment. These properties ensure the outputs are easier to refine and integrate into professional design workflows. Source Code: https://github.com/TomasGuija/DesigNet.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 7 Apr 2026.

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References · 21

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  11. 2021Im2Vec: Synthesizing Vector Graphics without Vector Supervisioncited by 116
  12. 2022VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Modelscited by 112
9 more
  1. 2023StarVector: Generating Scalable Vector Graphics Code from Images and Textcited by 102
  2. 2019A Learned Representation for Scalable Vector Graphicscited by 91
  3. 2024Text-to-Vector Generation with Neural Path Representationcited by 33
  4. 2023DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Qualitycited by 27
  5. 2023VecFusion: Vector Font Generation with Diffusioncited by 20
  6. 2024NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generationcited by 17
  7. 2023DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representationcited by 16
  8. 2025FontCraft: Multimodal Font Design Using Interactive Bayesian Optimizationcited by 7
  9. 2025Spline-based Transformerscited by 2

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