mlsanityData to 14 Oct 2026

Neural Strokes: Stylized Line Drawing of 3D Shapes

arXiv 2110.03900 ↗First posted 8 Oct 2021cs.CV cs.GRcited by 13

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Abstract (arXiv)

This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes, with variations in stroke thickness, deformation, and color learned from an artist's style. The model is fully differentiable. We train its parameters from a single training drawing of another 3D shape. We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours. Our method outputs the resulting drawing in a vector representation, enabling richer downstream analysis or editing in interactive applications.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 16 of its 45 references that are in the map. First posted on arXiv 8 Oct 2021.

Builds on

16 of 45 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  6. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  7. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  8. 2016Least Squares Generative Adversarial Networkscited by 1,001
  9. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  10. 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 639
  11. 2017Universal Style Transfer via Feature Transformscited by 354
  12. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 341
4 more
  1. 2018Synthesizing Programs for Images using Reinforced Adversarial Learningcited by 72
  2. 2020Why Do Line Drawings Work? A Realism Hypothesiscited by 28
  3. 2019Im2Pencil: Controllable Pencil Illustration from Photographscited by 18
  4. 2020Neural Contours: Learning to Draw Lines from 3D Shapescited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 3; 2025: 4; 2026: 2.

13 papers.

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  1. 2022Learning to generate line drawings that convey geometry and semanticscited by 83
  2. 2022CLIPascene: Scene Sketching with Different Types and Levels of Abstractioncited by 64
  3. 2023Breathing Life Into Sketches Using Text-to-Video Priorscited by 48
  4. 2024SketchAgent: Language-Driven Sequential Sketch Generationcited by 31
  5. 20243Doodle: Compact Abstraction of Objects with 3D Strokescited by 21
  6. 2023VecFusion: Vector Font Generation with Diffusioncited by 20
  7. 2025SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generationcited by 14
  8. 2025WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstractioncited by 4
  9. 2026VideoSketcher: Sequential Sketch Generation Using Video Model Priorscited by 2
  10. 2025Stroke2Sketch: Harnessing Stroke Attributes for Training-Free Sketch Generationcited by 1
  11. 2025ShadowDraw: From Any Object to Shadow-Drawing Compositional Artcited by 1
  12. 2024PS-StyleGAN: Illustrative Portrait Sketching using Attention-Based Style Adaptationcited by 0
1 more
  1. 2026Abstraction in Style: Beyond Texture and Colorcited by 0