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arXiv · 2103.15536

Cloud2Curve: Generation and Vectorization of Parametric Sketches

Ayan Das·Yongxin Yang·Timothy Hospedales·Tao Xiang·Yi-Zhe Song
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Abstract

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 a variable-degree B\'ezier 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 B\'ezier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.

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Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song. 2021-03-29. Cloud2Curve: Generation and Vectorization of Parametric Sketches. https://arxiv.org/abs/2103.15536

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