A Self-supervised Inverse Graphics Approach for Sketch Parametrization

Albert Suso*, Pau Riba, Oriol Ramos Terrades, Josep Lladós

*Corresponding author for this work

Research output: Book/ReportProceedingResearchpeer-review


The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets.

Original languageEnglish
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030861971
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12916 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Bézier curve
  • Chamfer distance
  • Inverse graphics
  • Sketch parametrization
  • Symbol recognition


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