TY - BOOK
T1 - A Self-supervised Inverse Graphics Approach for Sketch Parametrization
AU - Suso, Albert
AU - Riba, Pau
AU - Terrades, Oriol Ramos
AU - Lladós, Josep
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Bézier curve
KW - Chamfer distance
KW - Inverse graphics
KW - Sketch parametrization
KW - Symbol recognition
UR - http://www.scopus.com/inward/record.url?scp=85115299274&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-86198-8_3
DO - 10.1007/978-3-030-86198-8_3
M3 - Proceeding
AN - SCOPUS:85115299274
SN - 9783030861971
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
BT - A Self-supervised Inverse Graphics Approach for Sketch Parametrization
PB - Springer Science and Business Media Deutschland GmbH
ER -