BézierSketch: A generative model for scalable vector sketches
Song, Yi-Zhe (2020) BézierSketch: A generative model for scalable vector sketches In: ECCV 2020, 2020-08-23-2020-08-28, Glasgow, Scotland, UK.
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Abstract
The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing process. The landmark SketchRNN provided breakthrough by sequentially generating sketches as a sequence of waypoints. However this leads to low-resolution image generation, and failure to model long sketches. In this paper we present B´ezierSketch, a novel generative model for fully vector sketches that are automatically scalable and high-resolution. To this end, we first introduce a novel inverse graphics approach to stroke embedding that trains an encoder to embed each stroke to its best fit B´ezier curve. This enables us to treat sketches as short sequences of paramaterized strokes and thus train a recurrent sketch generator with greater capacity for longer sketches, while producing scalable high-resolution results. We report qualitative and quantitative results on the Quick, Draw! benchmark.
Item Type: | Conference or Workshop Item (Conference Poster) | ||||||
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Divisions : | Faculty of Engineering and Physical Sciences > Electronic Engineering | ||||||
Authors : |
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Date : | 2 July 2020 | ||||||
DOI : | 10.1007/978-3-030-58621-8 | ||||||
Copyright Disclaimer : | Copyright 2020 Springer Nature Switzerland AG | ||||||
Uncontrolled Keywords : | Sketch generation; Scalable graphics; B´ezier curve | ||||||
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Depositing User : | Diane Maxfield | ||||||
Date Deposited : | 07 Oct 2020 15:15 | ||||||
Last Modified : | 07 Oct 2020 15:15 | ||||||
URI: | http://epubs.surrey.ac.uk/id/eprint/858705 |
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