mlsanityData to 7 Oct 2026

NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation

arXiv 2405.15217 ↗First posted 24 May 2024cs.CV cs.GRcited by 17

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations, such as vector graphics. Unfortunately due to their variable structure and scarcity of vector training data, directly applying diffusion models on this domain remains a challenging problem. Using workarounds like optimization via Score Distillation Sampling (SDS) is also fraught with difficulty, as vector representations are non trivial to directly optimize and tend to result in implausible geometries such as redundant or self-intersecting shapes. NIVeL addresses these challenges by reinterpreting the problem on an alternative, intermediate domain which preserves the desirable properties of vector graphics -- mainly sparsity of representation and resolution-independence. This alternative domain is based on neural implicit fields expressed in a set of decomposable, editable layers. Based on our experiments, NIVeL produces text-to-vector graphics results of significantly better quality than the state-of-the-art.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 19 of its 36 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

19 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 18

  1. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  3. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  4. 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generationcited by 597
  5. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 550
  6. 2021Neural Fields in Visual Computing and Beyondcited by 539
  7. 2017A Neural Representation of Sketch Drawingscited by 311
  8. 2021CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoderscited by 186
  9. 2020DeepSVG: A Hierarchical Generative Network for Vector Graphics Animationcited by 158
  10. 2022VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Modelscited by 112
  11. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 101
  12. 2019A Learned Representation for Scalable Vector Graphicscited by 91
6 more
  1. 2023IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformerscited by 73
  2. 2018Synthesizing Programs for Images using Reinforced Adversarial Learningcited by 72
  3. 2019Deep Parametric Shape Predictions using Distance Fieldscited by 30
  4. 2023DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Qualitycited by 27
  5. 2021A Multi-Implicit Neural Representation for Fontscited by 21
  6. 2020Discovering Pattern Structure Using Differentiable Compositingcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 8.

17 papers.

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  1. 2024Empowering LLMs to Understand and Generate Complex Vector Graphicscited by 63
  2. 2025LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformercited by 43
  3. 2025NeuralSVG: An Implicit Representation for Text-to-Vector Generationcited by 16
  4. 2025SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generationcited by 16
  5. 2025SVGen: Interpretable Vector Graphics Generation with Large Language Modelscited by 13
  6. 2026Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedbackcited by 12
  7. 2026VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animationcited by 6
  8. 2025LINR Bridge: Vector Graphic Animation via Neural Implicits and Video Diffusion Priorscited by 2
  9. 2024SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generationcited by 1
  10. 2025Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibilitycited by 1
  11. 2026Stroke of Surprise: Progressive Semantic Illusions in Vector Sketchingcited by 1
  12. 2026GVR-Coder: A Visual-Feedback Framework for Structured SVG Generation in Complex Document and Meeting Scenarioscited by 1
5 more
  1. 2025Mean-Shift Distillation for Diffusion Mode Seekingcited by 0
  2. 2026SemLayer: Semantic-aware Generative Segmentation and Layer Construction for Abstract Iconscited by 0
  3. 2026DesigNet: Learning to Draw Vector Graphics as Designers Docited by 0
  4. 2026VecFontLLM: Anchor-Guided Direct Synthesis of Chinese Vector Fontscited by 0
  5. 2026SVG-Score: Human-Aligned Evaluation of Text-to-SVG Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
  2. 2023SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation
  3. 2023Neural Diffusion Models
  4. 2023Text-to-Image Diffusion Models are Zero-Shot Classifiers
  5. 2023Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
  6. 2025Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability
  7. 2023DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks
  8. 2024GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space