Abstract
This work introduces Self-Stylized Neural Painter (SSNP) creating stylized artworks in a stroke-by-stroke manner. SSNP consists of digit artist, canvas, style-stroke generator (SSG). By using SSG to generate style strokes, SSNP creates different styles paintings based on the given images. We design SSG as a three-player game based on a generative adversarial network to produce pure-color strokes that are crucial for mimicking the physical strokes. Furthermore, the digital artist adjusts parameters of strokes (shape, size, transparency, and color) to reconstruct as much detailed content of the reference image as possible to improve the fidelity.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - SIGGRAPH Asia 2021 Posters, SA 2021 |
| Editors | Shuzo John Shiota, Ayumi Kimura |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1-2 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450386876 |
| DOIs | |
| Publication status | Published - 14 Dec 2021 |
| Event | SIGGRAPH Asia 2021 - Computer Graphics and Interactive Techniques Conference, Asia - Tokyo, Japan Duration: 14 Dec 2021 → 17 Dec 2021 https://dl.acm.org/doi/proceedings/10.1145/3476124 |
Publication series
| Name | Proceedings - SIGGRAPH Asia |
|---|
Conference
| Conference | SIGGRAPH Asia 2021 - Computer Graphics and Interactive Techniques Conference, Asia |
|---|---|
| Country/Territory | Japan |
| City | Tokyo |
| Period | 14/12/21 → 17/12/21 |
| Internet address |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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