# Photo-Sketching: Inferring Contour Drawings from Images

arXiv: https://arxiv.org/abs/1901.00542 · HTML: https://mlsanity.com/paper/1901.00542/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 30 of 68)

- [Learning to generate line drawings that convey geometry and semantics](https://mlsanity.com/paper/2203.12691/) (2022)
- [Instruct-Imagen: Image Generation with Multi-modal Instruction](https://mlsanity.com/paper/2401.01952/) (2024)
- [CLIPascene: Scene Sketching with Different Types and Levels of Abstraction](https://mlsanity.com/paper/2211.17256/) (2022)
- [CLIPasso: Semantically-Aware Object Sketching](https://mlsanity.com/paper/2202.05822/) (2022)
- [DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models](https://mlsanity.com/paper/2306.14685/) (2023)
- [Breathing Life Into Sketches Using Text-to-Video Priors](https://mlsanity.com/paper/2311.13608/) (2023)
- [Control3D: Towards Controllable Text-to-3D Generation](https://mlsanity.com/paper/2311.05461/) (2023)
- [RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches](https://mlsanity.com/paper/2403.02709/) (2024)
- [Dense Extreme Inception Network for Edge Detection](https://mlsanity.com/paper/2112.02250/) (2021)
- [Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion Model](https://mlsanity.com/paper/2208.12675/) (2022)
- [More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2103.13990/) (2021)
- [Sketch Your Own GAN](https://mlsanity.com/paper/2108.02774/) (2021)
- [Picture that Sketch: Photorealistic Image Generation from Abstract Sketches](https://mlsanity.com/paper/2303.11162/) (2023)
- [SketchAgent: Language-Driven Sequential Sketch Generation](https://mlsanity.com/paper/2411.17673/) (2024)
- [A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch](https://mlsanity.com/paper/2208.03354/) (2022)
- [Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis](https://mlsanity.com/paper/2104.05703/) (2021)
- [Partially Does It: Towards Scene-Level FG-SBIR with Partial Input](https://mlsanity.com/paper/2203.14804/) (2022)
- [Emergent Graphical Conventions in a Visual Communication Game](https://mlsanity.com/paper/2111.14210/) (2021)
- [Thinking with Visual Abstract: Enhancing Multimodal Reasoning via Visual Abstraction](https://mlsanity.com/paper/2505.20164/) (2025)
- [SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation](https://mlsanity.com/paper/2502.08642/) (2025)
- [Improving Fairness in Image Classification via Sketching](https://mlsanity.com/paper/2211.00168/) (2022)
- [DiffSketching: Sketch Control Image Synthesis with Diffusion Models](https://mlsanity.com/paper/2305.18812/) (2023)
- [Multi-Object Sketch Animation with Grouping and Motion Trajectory Priors](https://mlsanity.com/paper/2508.15535/) (2025)
- [Deep Image Synthesis from Intuitive User Input: A Review and Perspectives](https://mlsanity.com/paper/2107.04240/) (2021)
- [Sketch-guided Image Inpainting with Partial Discrete Diffusion Process](https://mlsanity.com/paper/2404.11949/) (2024)
- [LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing](https://mlsanity.com/paper/2507.22627/) (2025)
- [SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches](https://mlsanity.com/paper/2209.00185/) (2022)
- ['CADSketchNet' -- An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural Networks](https://mlsanity.com/paper/2107.06212/) (2021)
- [High-Quality Pluralistic Image Completion via Code Shared VQGAN](https://mlsanity.com/paper/2204.01931/) (2022)
- [Learning Geometry-aware Representations by Sketching](https://mlsanity.com/paper/2304.08204/) (2023)

## Abstract (arXiv)

Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision. On one hand, they are the 2D elements that convey 3D shapes, on the other hand, they are indicative of occlusion events and thus separation of objects or semantic concepts. In this paper, we aim to generate contour drawings, boundary-like drawings that capture the outline of the visual scene. Prior art often cast this problem as boundary detection. However, the set of visual cues presented in the boundary detection output are different from the ones in contour drawings, and also the artistic style is ignored. We address these issues by collecting a new dataset of contour drawings and proposing a learning-based method that resolves diversity in the annotation and, unlike boundary detectors, can work with imperfect alignment of the annotation and the actual ground truth. Our method surpasses previous methods quantitatively and qualitatively. Surprisingly, when our model fine-tunes on BSDS500, we achieve the state-of-the-art performance in salient boundary detection, suggesting contour drawing might be a scalable alternative to boundary annotation, which at the same time is easier and more interesting for annotators to draw.