A Self-supervised Inverse Graphics Approach for Sketch Parametrization

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

*Autor corresponent d’aquest treball

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Resum

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.

Idioma originalAnglès
EditorSpringer Science and Business Media Deutschland GmbH
Nombre de pàgines15
ISBN (imprès)9783030861971
DOIs
Estat de la publicacióPublicada - 2021

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum12916 LNCS
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349

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