TY  - CONF
AU  - Albert Suso
AU  - Pau Riba
AU  - Oriol Ramos Terrades
AU  - Josep Llados
A2  - ICDAR
PY  - 2021//
TI  - A Self-supervised Inverse Graphics Approach for Sketch Parametrization
T2  - LNCS
BT  - 16th International Conference on Document Analysis and Recognition
SP  - 28
EP  - 42
VL  - 12916
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.
UR  - https://link.springer.com/chapter/10.1007/978-3-030-86198-8_3
N1  - DAG; 600.121
ID  - Albert Suso2021
ER  -