TY - GEN
T1 - Emotional Conveyance Analysis of Artificial Intelligence Painting
AU - Ma, Lin
AU - Chen, Dengkai
AU - Feng, Yuan
AU - Hou, Xinggang
AU - Chen, Jing
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023/11/13
Y1 - 2023/11/13
N2 - AI-generated content inherits the strengths of Professional-Generated Content (PGC) and User-Generated Content (UGC) while fully leveraging technological advantages to forge innovative digital content generation and interaction. With the continuous evolution of technology, AI painting techniques have sparked widespread discussions in the realm of creative expression. Artificial intelligence technology, driven by its ability to comprehend semantics, facilitates the conversion of images and conveys semantic emotions. It also contributes to establishing a robust human-machine interactive relationship, albeit accompanied by certain ethical risks. This study employs emotional measurement experiments and eye-tracking technology. By analyzing individuals' assessments of emotional mixed-image collections and correlating the results with experimental eye-tracking data and dominant color patterns within the images, the paper investigates the accuracy of AI-generated tools in transcribing emotional nuances. Furthermore, it delves into the synergistic relationship between humans and machines within the context of artistic creation and explores the dynamics of their interaction.
AB - AI-generated content inherits the strengths of Professional-Generated Content (PGC) and User-Generated Content (UGC) while fully leveraging technological advantages to forge innovative digital content generation and interaction. With the continuous evolution of technology, AI painting techniques have sparked widespread discussions in the realm of creative expression. Artificial intelligence technology, driven by its ability to comprehend semantics, facilitates the conversion of images and conveys semantic emotions. It also contributes to establishing a robust human-machine interactive relationship, albeit accompanied by certain ethical risks. This study employs emotional measurement experiments and eye-tracking technology. By analyzing individuals' assessments of emotional mixed-image collections and correlating the results with experimental eye-tracking data and dominant color patterns within the images, the paper investigates the accuracy of AI-generated tools in transcribing emotional nuances. Furthermore, it delves into the synergistic relationship between humans and machines within the context of artistic creation and explores the dynamics of their interaction.
KW - AI-generated image
KW - emotional conveyance
KW - eye movement experiment
KW - human-AI collaboration
KW - image emotion analysis
KW - visual focus
UR - http://www.scopus.com/inward/record.url?scp=85187238465&partnerID=8YFLogxK
U2 - 10.1145/3629606.3629612
DO - 10.1145/3629606.3629612
M3 - 会议稿件
AN - SCOPUS:85187238465
T3 - ACM International Conference Proceeding Series
SP - 44
EP - 54
BT - Proceedings of the 11th International Symposium of Chinese CHI
PB - Association for Computing Machinery
T2 - 11th International Symposium of Chinese CHI, Chinese CHI 2023
Y2 - 13 November 2023 through 16 November 2023
ER -