demonstration

AI Painting: An Aesthetic Painting Generation System

Authors:

Cunjun Zhang,

Kehua Lei,

Jia Jia,

Yihui Ma,

Zhiyuan HuAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 1231 - 1233

https://doi.org/10.1145/3240508.3241386

Published: 15 October 2018 Publication History

Get Access

Abstract

There are many great works done in image generation. However, it is still an open problem how to generate a painting, which is meeting the aesthetic rules in specific style. Therefore, in this paper, we propose a demonstration to generate a specific painting based on users' input. In the system called AI Painting, we generate an original image from content text, transfer the image into a specific aesthetic effect, simulate the image into specific artistic genre, and illustrate the painting process.

References

[1]

Alexei A. Efros and William T. Freeman. 2001. Image quilting for texture synthesis and transfer. In Conference on Computer Graphics and Interactive Techniques. 341--346.

Digital Library

Google Scholar

[2]

A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. 2009. Describing objects by their attributes. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 1778--1785.

Google Scholar

[3]

Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image Style Transfer Using Convolutional Neural Networks. In Computer Vision and Pattern Recognition. 2414--2423.

Google Scholar

[4]

Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra. 2015. DRAW: a recurrent neural network for image generation. Computer Science (2015), 1462--1471.

Digital Library

Google Scholar

[5]

Aaron Hertzmann. 1998. Painterly rendering with curved brush strokes of multiple sizes. In Conference on Computer Graphics and Interactive Techniques. 453--460.

Digital Library

Google Scholar

[6]

Xun Huang and Serge Belongie. 2017. Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. (2017).

Google Scholar

[7]

Lingyu Liang and Lianwen Jin. 2013. Image-Based Rendering for Ink Painting. (2013), 3950--3954.

Digital Library

Google Scholar

[8]

Yihui Ma, Jia Jia, Yufan Hou, Yaohua Bu, and Wentao Han. 2018. Understanding the Aesthetic Styles of Social Images. In Acoustics, Speech and Signal Processing (ICASSP), 2018 IEEE International Conference on. IEEE.

Crossref

Google Scholar

[9]

Yihui Ma, Jia Jia, Suping Zhou, Jingtian Fu, Yejun Liu, and Zijian Tong. 2017. Towards Better Understanding the Clothing Fashion Styles: A Multimodal Deep Learning Approach. (2017).

Google Scholar

[10]

Attila Neumann. 2005. Color style transfer techniques using hue, lightness and saturation histogram matching. In Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging. 111--122.

Digital Library

Google Scholar

[11]

Devi Parikh and Kristen Grauman. 2011. Relative attributes. Iccv 6669, 5 (2011), 503--510.

Digital Library

Google Scholar

[12]

Xiaohui Wang, Jia Jia, Jiaming Yin, and Lianhong Cai. 2014. Interpretable aesthetic features for affective image classification. In IEEE International Conference on Image Processing. 3230--3234.

Google Scholar

[13]

Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris Metaxas. 2017. StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks. arXiv preprint arXiv:1710.10916 (2017).

Google Scholar

Cited By

View all

Carrasco MGonzález-Martín CNavajas-Torrente SDastres R(2024)Level of Agreement between Emotions Generated by Artificial Intelligence and Human Evaluation: A Methodological ProposalElectronics10.3390/electronics1320401413:20(4014)Online publication date: 12-Oct-2024
https://doi.org/10.3390/electronics13204014
Shi TChen CLi XHao A(2024)Semantic and style based multiple reference learning for artistic and general image aesthetic assessmentNeurocomputing10.1016/j.neucom.2024.127434582(127434)Online publication date: May-2024
https://doi.org/10.1016/j.neucom.2024.127434
San TKanematsu YMikami K(2023)Algorithmic Analysis of Color Combinations Principle in Game Concept ArtThe Journal of the Society for Art and Science10.3756/artsci.22.15_122:4(15_1-15_7)Online publication date: 2023
https://doi.org/10.3756/artsci.22.15_1
Show More Cited By

Index Terms

AI Painting: An Aesthetic Painting Generation System
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces
  2. Interaction design
    1. Interaction design process and methods
      1. User interface design

Recommendations

Digital Painting Techniques: Practical Techniques of Digital Art Masters
PAINTING FOR PHOTOGRAPHERS-Steps and Art Lessons for painting Photos in Corel Painter and Adobe Photoshop
Ndebele Painting in VR

The Ndebele tribe of southern Africa consists of four subtribes. The Ndzundza is the only Ndebele group practising the decorative arts of painting and beadwork. The craft of producing striking, colourful and geometrical artwork has become their most ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

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.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2018

Check for updates

Author Tags

Qualifiers

Demonstration

Funding Sources

National Key Research and Develop- ment Plan
the Innovation Method Fund of China

Conference

MM '18

Sponsor:

SIGMM

MM '18: ACM Multimedia Conference

October 22 - 26, 2018

Seoul, Republic of Korea

Acceptance Rates

MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
986
Total Downloads

Downloads (Last 12 months)130
Downloads (Last 6 weeks)5

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Carrasco MGonzález-Martín CNavajas-Torrente SDastres R(2024)Level of Agreement between Emotions Generated by Artificial Intelligence and Human Evaluation: A Methodological ProposalElectronics10.3390/electronics1320401413:20(4014)Online publication date: 12-Oct-2024
https://doi.org/10.3390/electronics13204014
Shi TChen CLi XHao A(2024)Semantic and style based multiple reference learning for artistic and general image aesthetic assessmentNeurocomputing10.1016/j.neucom.2024.127434582(127434)Online publication date: May-2024
https://doi.org/10.1016/j.neucom.2024.127434
San TKanematsu YMikami K(2023)Algorithmic Analysis of Color Combinations Principle in Game Concept ArtThe Journal of the Society for Art and Science10.3756/artsci.22.15_122:4(15_1-15_7)Online publication date: 2023
https://doi.org/10.3756/artsci.22.15_1
Xu JZhang XLi HYoo CPan Y(2023)Is Everyone an Artist? A Study on User Experience of AI-Based Painting SystemApplied Sciences10.3390/app1311649613:11(6496)Online publication date: 26-May-2023
https://doi.org/10.3390/app13116496
Yi RTian HGu ZLai YRosin P(2023)Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.02144(22388-22397)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.02144
Bengamra SMzoughi OBigand AZagrouba E(2023)A comprehensive survey on object detection in Visual Art: taxonomy and challengeMultimedia Tools and Applications10.1007/s11042-023-15968-983:5(14637-14670)Online publication date: 3-Jul-2023
https://doi.org/10.1007/s11042-023-15968-9
Sun YLyu YLin PLin R(2022)Comparison of Cognitive Differences of Artworks between Artist and Artistic Style TransferApplied Sciences10.3390/app1211552512:11(5525)Online publication date: 29-May-2022
https://doi.org/10.3390/app12115525
Zhang JMiao YYu J(2021)A Comprehensive Survey on Computational Aesthetic Evaluation of Visual Art Images: Metrics and ChallengesIEEE Access10.1109/ACCESS.2021.30830759(77164-77187)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3083075
Xing BSi HChen JYe MShi L(2021)Computational model for predicting user aesthetic preference for GUI using DCNNsCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-021-00064-43:2(147-169)Online publication date: 21-Apr-2021
https://doi.org/10.1007/s42486-021-00064-4
Sun YLyu YLin PLin R(undefined)Comparison of Cognitive Differences of Artworks between Artist and Artistic Style TransferSSRN Electronic Journal10.2139/ssrn.3988565
https://doi.org/10.2139/ssrn.3988565

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Digital Painting Techniques: Practical Techniques of Digital Art Masters

PAINTING FOR PHOTOGRAPHERS-Steps and Art Lessons for painting Photos in Corel Painter and Adobe Photoshop

Ndebele Painting in VR