# Creative Sketch Generation

arXiv: https://arxiv.org/abs/2011.10039 · HTML: https://mlsanity.com/paper/2011.10039/

## Key facts

- Cited by 44 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 30 of 44)

- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [CLIPascene: Scene Sketching with Different Types and Levels of Abstraction](https://mlsanity.com/paper/2211.17256/) (2022)
- [DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models](https://mlsanity.com/paper/2306.14685/) (2023)
- [FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context](https://mlsanity.com/paper/2203.02113/) (2022)
- [SketchAgent: Language-Driven Sequential Sketch Generation](https://mlsanity.com/paper/2411.17673/) (2024)
- [CodeS: Natural Language to Code Repository via Multi-Layer Sketch](https://mlsanity.com/paper/2403.16443/) (2024)
- [ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints](https://mlsanity.com/paper/2308.02669/) (2023)
- [Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches](https://mlsanity.com/paper/2203.14843/) (2022)
- [The Animation Transformer: Visual Correspondence via Segment Matching](https://mlsanity.com/paper/2109.02614/) (2021)
- [Partially Does It: Towards Scene-Level FG-SBIR with Partial Input](https://mlsanity.com/paper/2203.14804/) (2022)
- [DoodleFormer: Creative Sketch Drawing with Transformers](https://mlsanity.com/paper/2112.03258/) (2021)
- [AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints](https://mlsanity.com/paper/2205.10636/) (2022)
- [Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings](https://mlsanity.com/paper/2303.11502/) (2023)
- [Can AI Be as Creative as Humans?](https://mlsanity.com/paper/2401.01623/) (2024)
- [TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Sampling](https://mlsanity.com/paper/2310.01819/) (2023)
- [ChiroDiff: Modelling chirographic data with Diffusion Models](https://mlsanity.com/paper/2304.03785/) (2023)
- [SketchINR: A First Look into Sketches as Implicit Neural Representations](https://mlsanity.com/paper/2403.09344/) (2024)
- [PartCraft: Crafting Creative Objects by Parts](https://mlsanity.com/paper/2407.04604/) (2024)
- [Improving Fairness in Image Classification via Sketching](https://mlsanity.com/paper/2211.00168/) (2022)
- [SketchXAI: A First Look at Explainability for Human Sketches](https://mlsanity.com/paper/2304.11744/) (2023)
- [SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation](https://mlsanity.com/paper/2308.14191/) (2023)
- [One Sketch for All: One-Shot Personalized Sketch Segmentation](https://mlsanity.com/paper/2112.10838/) (2021)
- [ContextSeg: Sketch Semantic Segmentation by Querying the Context with Attention](https://mlsanity.com/paper/2311.16682/) (2023)
- [Freehand Sketch Generation from Mechanical Components](https://mlsanity.com/paper/2408.05966/) (2024)
- [DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination](https://mlsanity.com/paper/2311.15477/) (2023)
- [Instance Segmentation of Scene Sketches Using Natural Image Priors](https://mlsanity.com/paper/2502.09608/) (2025)
- [ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schr"odinger Bridge](https://mlsanity.com/paper/2311.14542/) (2023)
- [Calliar: An Online Handwritten Dataset for Arabic Calligraphy](https://mlsanity.com/paper/2106.10745/) (2021)
- [Do Generalised Classifiers really work on Human Drawn Sketches?](https://mlsanity.com/paper/2407.03893/) (2024)
- [StrokeFusion: Vector Sketch Generation via Joint Stroke-UDF Encoding and Latent Sequence Diffusion](https://mlsanity.com/paper/2503.23752/) (2025)

## Abstract (arXiv)

Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that are quite mundane. In this work, we introduce two datasets of creative sketches -- Creative Birds and Creative Creatures -- containing 10k sketches each along with part annotations. We propose DoodlerGAN -- a part-based Generative Adversarial Network (GAN) -- to generate unseen compositions of novel part appearances. Quantitative evaluations as well as human studies demonstrate that sketches generated by our approach are more creative and of higher quality than existing approaches. In fact, in Creative Birds, subjects prefer sketches generated by DoodlerGAN over those drawn by humans! Our code can be found at https://github.com/facebookresearch/DoodlerGAN and a demo can be found at http://doodlergan.cloudcv.org.