1st Edition
Artificial Intelligence for Art Creation and Understanding
AI-Generated Content (AIGC) is a revolutionary engine for digital content generation. In the area of art, AI has achieved most remarkable advancements. AI is capable of not only creating paintings or music comparable to human masterpieces, but also understanding and appreciating art works. For both professionals and amateurs, AI is an enabling tool and a companion to open and enjoy a new world of art.
This book aims to present to the readers the state-of-the-art AI technologies for art creation, understanding, and evaluation. The deliberately chosen contents include a survey on cross-modal generation of visual and auditory content, explainable AI and music, AI enabled robotic theatre for Chinese folk art, AI for ancient Chinese music restoration and reproduction, AI for brainwave opera, artistic text style transfer, data-driven automatic choreography, Human-AI collaborative sketching, personalized music recommendation and generation based on emotion and memory (MemoMusic), understanding music and emotion from brain, music question answering, emotional quality evaluation for generated music, and AI for image aesthetic evaluation.
The key features of the book is as follows:
- AI for Art is a fascinating cross-disciplinary field for the academic community and the public as well.
- Each chapter is a relatively independent but rather interesting topic, which provides an entry for corresponding readers.
- It presents SOTA AI technologies for art creation and understanding.
- The artistry and appreciation of the book is great. For example, the combination of AI with traditional Chinese art is very fascinating.
This book is dedicated to the international cross-disciplinary AI Art community composed of professors, students, researchers, and engineers from AI (machine learning, computer vision, multimedia computing, affective computing, robotics, etc.), art (painting, music, dance, fashion, design, etc.), cognition science, and psychology. Also, general audience can also benefit from reading part of the book.
Chapter 1. Explainable AI and Music
Nick Bryan-Kinns, Berker Banar, Corey Ford, Courtney N Reed, Yixiao Zhang, and Jack Armitage
Chapter 2. AI Enabled Robotic Theatres for Chinese Folk Art
Haipeng Mi, Yuan Yao, Zhihao Yao, Qirui Sun, Hanxuan Li, Mingyue Gao, Beituo Liu, and Yao Lu
Chapter 3. AIBO – Or How to Make a ‘Sicko’ Brainwave Opera
Ellen Pearlman
Chapter 4. Cross-modal Generation of Visual and Auditory Content: A Survey
Feng Gao, Mengting Liu, and Ying Zhou
Chapter 5. Artistic Text Style Transfer
Shuai Yang, Zhengbo Xu, Wenjing Wang, and Jiaying Liu
Chapter 6. Data-driven Automatic Choreography
Rongfeng Li
Chapter 7. Toward Human-AI Collaborative Sketching
Zeyu Wang, Tuanfeng Y. Wang, and Julie Dorsey
Chapter 8. MemoMusic: A Personalized Music Recommendation and Generation Framework Based on Emotion and Memory
Luntian Mou, Yihan Sun, Yunhan Tian, Jueying Li, Juehui Li, Yiqi Sun, Yuhang Liu, Zexi Zhang, Ruichen He, Zijin Li, and Ramesh Jain
Chapter 9. Algorithmic Composition for Ancient Chinese Music Restoration and Reproduction – A Melody Generator Approach
Tiange Zhou
Chapter 10. Understanding Music and Emotion from Brain
Haifeng Li, Hongjian Bo, Lin Ma, Jing Chen, and Hongwei Li
Chapter 11. Music Question Answering: Cognize and Perceive Music
Yun Tie, Wenhao Gao, Xiaobing Li, Di Lu, Qingwen Zhou, Jiessie Tie, and Lin Qi
Chapter 12. Emotional Quality Evaluation for Generated Music
Wei Zhong, Hongfei Wang, Lin Ma, Long Ye, and Qin Zhang
Chapter 13. A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction
Xin Jin, Xinning Li, Heng Huang, Xiaodong Li, Chaoen Xiao, and Xiqiao Li
Biography
Luntian Mou received the Ph.D. degree in computer science from the University of Chinese Academy of Sciences, China in 2012. He accomplished as a postdoctoral Researcher with the Institute of Digital Media, Peking University, China in 2014. From 2019 to 2020, he served as a visiting scholar at Donald Bren School of Information and Computer Sciences, University of California, Irvine, USA. He is currently an associate professor with the Beijing Institute of Artificial Intelligence, Beijing University of Technology, China. His research interests include artificial intelligence, machine learning, pattern recognition, affective computing, multimedia computing, and brain-like computing. He has published on renowned journals such as TAFFC, TMM, TOMM, and ESWA. He is the recipient of Beijing Municipal Science and Technology Advancement Award, IEEE Outstanding Contribution to Standardization Award, and AVS Outstanding Contribution on 15th Anniversary Award. He serves as a guest editor for Machine Intelligence Research, and a reviewer for many important international journals and conferences such as TIP, TAFFC, TCSVT, TITS, AAAI, etc. He is a senior member of IEEE and CCF, and a member of ACM. He is the chair of the organizing committee of the 2023 CSIG Conference on Emotional Intelligence (CEI). He is the founding chair of IEEE Workshop on Artificial Intelligence for Art Creation (AIART).
