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 BézierSketch, 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 Bézier 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.
Original language | English |
---|---|
Title of host publication | Computer Vision – ECCV 2020 |
Subtitle of host publication | 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVI |
Publisher | Springer, Cham |
Pages | 632-647 |
Number of pages | 19 |
ISBN (Electronic) | 978-3-030-58574-7 |
ISBN (Print) | 978-3-030-58573-0 |
DOIs | |
Publication status | Published - 13 Nov 2020 |
Event | 16th European Conference on Computer Vision - Virtual conference Duration: 23 Aug 2020 → 28 Aug 2020 https://eccv2020.eu/ |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer, Cham |
Volume | 12371 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 16th European Conference on Computer Vision |
---|---|
Abbreviated title | ECCV 2020 |
City | Virtual conference |
Period | 23/08/20 → 28/08/20 |
Internet address |
Keywords
- Sketch generation
- Scalable graphics
- Bézier curve