skip to main content
10.1145/3476124.3488617acmconferencesArticle/Chapter ViewAbstractPublication Pagessiggraph-asiaConference Proceedingsconference-collections
poster

Self-Stylized Neural Painter

Published: 14 December 2021 Publication History

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.

Supplementary Material

Poster (sa21-2_Final_Poster_Print-Ready.pdf)
Appendix (sa21-2_Final_Poster_Appendix.pdf)
MP4 File (ssnp_painting_example.mp4)
Supplemental video
MP4 File (3476124.3488617.mp4)
presentation

References

[1]
Aaron Hertzmann. 1998. Painterly Rendering with Curved Brush Strokes of Multiple Sizes. In ACM SIGGRAPH. 453–460.
[2]
Zhewei Huang, Wen Heng, and Shuchang Zhou. 2019. Learning to Paint With Model-Based Deep Reinforcement Learning. In IEEE International Conference on Computer Vision. 8708–8717.
[3]
Reiichiro Nakano. 2019. Neural Painters: A learned differentiable constraint for generating brushstroke paintings. In Neural Information Processing Systems Workshops.
[4]
A. Radford, Luke Metz, and Soumith Chintala. 2016. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In International Conference on Learning Representations. 1–16.
[5]
Zhengxia Zou, Tianyang Shi, Shuang Qiu, Yi Yuan, and Zhenwei Shi. 2021. Stylized Neural Painting. In IEEE Conference on Computer Vision and Pattern Recognition. Virtual, 15689–15698.

Cited By

View all
  • (2023)Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networksComputational Visual Media10.1007/s41095-022-0287-39:4(787-806)Online publication date: 11-Mar-2023

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SA '21 Posters: SIGGRAPH Asia 2021 Posters
December 2021
87 pages
ISBN:9781450386876
DOI:10.1145/3476124
  • Editors:
  • Shuzo John Shiota,
  • Ayumi Kimura,
  • Wan-Chun Alex Ma
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2021

Check for updates

Qualifiers

  • Poster
  • Research
  • Refereed limited

Funding Sources

  • The Hong Kong Polytechnic University under Grant
  • Macao Science and Technology Development Fund under Macao Funding Scheme for Key R \& D Projects

Conference

SA '21
Sponsor:
SA '21: SIGGRAPH Asia 2021
December 14 - 17, 2021
Tokyo, Japan

Acceptance Rates

Overall Acceptance Rate 178 of 869 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)35
  • Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networksComputational Visual Media10.1007/s41095-022-0287-39:4(787-806)Online publication date: 11-Mar-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media