Self-Stylized Neural Painter

Qian Wang; Cai Guo; Hong Ning Dai; Ping Li

doi:10.1145/3476124.3488617

Self-Stylized Neural Painter

Qian Wang, Cai Guo, Hong Ning Dai, Ping Li

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

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	https://doi.org/10.1145/3476124.3488617
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	https://dl.acm.org/doi/proceedings/10.1145/3476124

Scopus Subject Areas

Computer Graphics and Computer-Aided Design
Human-Computer Interaction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3476124.3488617

Cite this

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title = "Self-Stylized Neural Painter",

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.",

author = "Qian Wang and Cai Guo and Dai, {Hong Ning} and Ping Li",

note = "Funding Information: The work was supported by Macau Science and Technology Development Fund under Key R &D Projects (0025/2019/AKP), and by The Hong Kong Polytechnic University (P0030419). Publisher Copyright: {\textcopyright} 2021 Owner/Author.; SIGGRAPH Asia 2021 - Computer Graphics and Interactive Techniques Conference, Asia ; Conference date: 14-12-2021 Through 17-12-2021",

year = "2021",

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language = "English",

series = "Proceedings - SIGGRAPH Asia",

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pages = "1--2",

editor = "Shiota, {Shuzo John} and Ayumi Kimura",

booktitle = "Proceedings - SIGGRAPH Asia 2021 Posters, SA 2021",

address = "United States",

url = "https://dl.acm.org/doi/proceedings/10.1145/3476124",

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Wang, Q, Guo, C, Dai, HN & Li, P 2021, Self-Stylized Neural Painter. in SJ Shiota & A Kimura (eds), Proceedings - SIGGRAPH Asia 2021 Posters, SA 2021., 9, Proceedings - SIGGRAPH Asia, Association for Computing Machinery (ACM), pp. 1-2, SIGGRAPH Asia 2021 - Computer Graphics and Interactive Techniques Conference, Asia, Tokyo, Japan, 14/12/21. https://doi.org/10.1145/3476124.3488617

TY - GEN

T1 - Self-Stylized Neural Painter

AU - Wang, Qian

AU - Guo, Cai

AU - Dai, Hong Ning

AU - Li, Ping

N1 - Funding Information: The work was supported by Macau Science and Technology Development Fund under Key R &D Projects (0025/2019/AKP), and by The Hong Kong Polytechnic University (P0030419). Publisher Copyright: © 2021 Owner/Author.

PY - 2021/12/14

Y1 - 2021/12/14

N2 - 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.

AB - 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.

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DO - 10.1145/3476124.3488617

M3 - Conference proceeding

AN - SCOPUS:85123783758

T3 - Proceedings - SIGGRAPH Asia

SP - 1

EP - 2

BT - Proceedings - SIGGRAPH Asia 2021 Posters, SA 2021

A2 - Shiota, Shuzo John

A2 - Kimura, Ayumi

PB - Association for Computing Machinery (ACM)

T2 - SIGGRAPH Asia 2021 - Computer Graphics and Interactive Techniques Conference, Asia

Y2 - 14 December 2021 through 17 December 2021

ER -

Self-Stylized Neural Painter

Abstract

Publication series

Conference

Scopus Subject Areas

UN SDGs

Access to Document

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