# Cloud2Curve: Generation and Vectorization of Parametric Sketches

arXiv: https://arxiv.org/abs/2103.15536 · HTML: https://mlsanity.com/paper/2103.15536/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation](https://mlsanity.com/paper/2007.11301/) (2020)
- [Deep Convolutional Inverse Graphics Network](https://mlsanity.com/paper/1503.03167/) (2015)
- [A Learned Representation for Scalable Vector Graphics](https://mlsanity.com/paper/1904.02632/) (2019)
- [Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval](https://mlsanity.com/paper/1904.03451/) (2019)
- [Sketchformer: Transformer-based Representation for Sketched Structure](https://mlsanity.com/paper/2002.10381/) (2020)
- [Synthesizing Programs for Images using Reinforced Adversarial Learning](https://mlsanity.com/paper/1804.01118/) (2018)
- [Deep Neural Decision Trees](https://mlsanity.com/paper/1806.06988/) (2018)
- [Deep Learning for Free-Hand Sketch: A Survey](https://mlsanity.com/paper/2001.02600/) (2020)
- [Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2002.10310/) (2020)
- [Learning to Sketch with Shortcut Cycle Consistency](https://mlsanity.com/paper/1805.00247/) (2018)
- [B'ezierSketch: A generative model for scalable vector sketches](https://mlsanity.com/paper/2007.02190/) (2020)
- [CoSE: Compositional Stroke Embeddings](https://mlsanity.com/paper/2006.09930/) (2020)
- [SketchEmbedNet: Learning Novel Concepts by Imitating Drawings](https://mlsanity.com/paper/2009.04806/) (2020)
- [Scones: Towards Conversational Authoring of Sketches](https://mlsanity.com/paper/2005.07781/) (2020)

## Built on by (most cited first, 16 of 16)

- [SketchGen: Generating Constrained CAD Sketches](https://mlsanity.com/paper/2106.02711/) (2021)
- [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)
- [How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?](https://mlsanity.com/paper/2403.07203/) (2024)
- [CraftSVG: Multi-Object Text-to-SVG Synthesis via Layout Guided Diffusion](https://mlsanity.com/paper/2404.00412/) (2024)
- [SVG-Net: An SVG-based Trajectory Prediction Model](https://mlsanity.com/paper/2110.03706/) (2021)
- [Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition](https://mlsanity.com/paper/2311.18254/) (2023)
- [DeepIcon: A Hierarchical Network for Layer-wise Icon Vectorization](https://mlsanity.com/paper/2410.15760/) (2024)
- [StrokeFusion: Vector Sketch Generation via Joint Stroke-UDF Encoding and Latent Sequence Diffusion](https://mlsanity.com/paper/2503.23752/) (2025)
- [Inkorrect: Online Handwriting Spelling Correction](https://mlsanity.com/paper/2202.13794/) (2022)
- [Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art](https://mlsanity.com/paper/2311.10129/) (2023)
- [On the Temporality for Sketch Representation Learning](https://mlsanity.com/paper/2512.04007/) (2025)
- [AnchorFlow: Learning Anchor Placement for Faithful and Editable SVG Reconstruction](https://mlsanity.com/paper/2605.19551/) (2026)
- [Character Generation through Self-Supervised Vectorization](https://mlsanity.com/paper/2208.02012/) (2022)
- [Adversarial Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights](https://mlsanity.com/paper/2311.15994/) (2023)
- [Annotation-Free Human Sketch Quality Assessment](https://mlsanity.com/paper/2507.20548/) (2025)

## Abstract (arXiv)

Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raster or waypoint based stroke encoded as a point-cloud with a variable-degree B'ezier curve. Building on this module, we present Cloud2Curve, a generative model for scalable high-resolution vector sketches that can be trained end-to-end using point-cloud data alone. As a consequence, our model is also capable of deterministic vectorization which can map novel raster or waypoint based sketches to their corresponding high-resolution scalable B'ezier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.