Cloud2Curve: Generation and Vectorization of Parametric Sketches

Ayan Das, Yongxin Yang, Timothy M Hospedales, Tao Xiang, Yi-Zhe Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 avariable-degree Bézier 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 Bézier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.
Original languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition (CVPR 2021)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
Publication statusAccepted/In press - 1 Mar 2021
EventIEEE Conference on Computer Vision and Pattern Recognition 2021 - Virtual
Duration: 19 Jun 202125 Jun 2021


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2021
Abbreviated titleCVPR 2021
Internet address

Fingerprint Dive into the research topics of 'Cloud2Curve: Generation and Vectorization of Parametric Sketches'. Together they form a unique fingerprint.

Cite this