Computer Science ›› 2024, Vol. 51 ›› Issue (11): 47-53.doi: 10.11896/jsjkx.240700085

• Social Media Fake News Detection • Previous Articles     Next Articles

Analysis of User Evaluation Indicator for AIGC Digital Illustration Design Principles

XU Jun1, ZHOU Peijin1, ZHANG Haijing1, ZHANG Hao2, XU Yuzhong1   

  1. 1 College of Design and Architecture,Zhejiang University of Technology,Hangzhou 310000,China
    2 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310000,China
  • Received:2024-07-10 Revised:2024-08-29 Online:2024-11-15 Published:2024-11-06
  • About author:XU Jun,born in 1979,master,senior experimentalist,master’s supervisor,is a premium member of CCF(No.27493S).His main research interests include digital media art and digital humanities.
    XU Yuzhong,born in 1970,master,professor,master’s supervisor.His main research interests include digital media art and design aesthetics.
  • Supported by:
    National Social Science Fundation of China(22BMZ038).

Abstract: Based on the digital illustration design principles and the technical principles of AIGC,a production process from lite-rary text to digital illustration has been constructed.Through experiments,the actual effects and existing problems of AIGC digi-tal illustration have been demonstrated.The current development status of digital illustration has been reviewed,and the relationship between AIGC and digital illustration design principles has been analyzed.The main processes and key technologies currently used for digital illustration with AIGC have been summarized and introduced.Then,multiple AI algorithms have been used to build a production process from literary text to digital illustration,and multiple sets of experiments have been conducted.Finally,a questionnaire has been designed based on indicators such as the degree of fit between text and image to evaluate users,and analyze the generation rules,characteristics,and usability of AIGC digital illustration.AIGC can meet certain narrative and artistic style requirements,but its effectiveness decreases as the narrative nature of the text increases.At the same time,it has poor performance for rare content,and its image details cannot represent complex narrative scenarios.Through theoretical analysis and experimental comparison,it can be concluded that AIGC has great advantages in terms of production efficiency with the help of artificial intelligence technology in digital illustration.However,due to a lack of understanding of narrative content,there are shortcomings in its image expression.Currently,it still relies on the high involvement of designers to solve practical problems,and it also requires collaboration from all parties to promote the healthy development of new technologies.

Key words: Digital illustration, Artificial intelligence generated content, Artificial intelligence, Narrative

CLC Number: 

  • TP391.41
[1] LIU T.The Construction and Production of Cultural Imagery:An Analysis of the Psychological Operation Mechanism of Vi-sual Rhetoric[J].Modern Communication,2011(9):20-25.
[2] SHI Y.Communication and Thinking of Visual Language in the Image Age[J].Media,2015,209(12):69-71.
[3] WANG S C,PANG H,XIONG Y R,et al.Image Digital Intelli-gence:Phenomenon and Its Interpretation[J].Zhejiang Social Sciences,2023(3):84-93,159.
[4] XIA Z N,CHEN Z L.Cihai[M].Shanghai:Shanghai Dictionary Press,2009:233.
[5] YAO H.Illustration Analysis and Rheological Investigation[J].Nanjing University of the Arts(Art and Design Edition),2011(5):83-85,177.
[6] CHEN Q.On the Art of Illustration in the Digital Age[J].Shanxi Normal University(Philosophy and Social Sciences Edition),2007(S2):158-160.
[7] WANG J H,WANG W L.Research on Modern Digital Illustration[J].Art Education Research,2018(14):16-17.
[8] Artificial Intelligence Generated Content (AIGC) white paper(2022)[EB/OL].[2022-09-04].http://www.cbdio.com/BigData/2022-09/04/content_6170457.htm.
[9] GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative Adversarial Networks[J].Communications of the ACM,2020,63(11):139-144.
[10] HO J,JAIN A,ABBEEL P.Denoising Diffusion ProbabilisticModels[J].Advances in Neural Information Processing Systems,2020,33:6840-6851.
[11] Garnter.Generative AI,Machine Customers and AR/VR areExpected to Transform Sales in the Next Five Years[EB/OL].[2022-11-05].https://www.gartner.com/en/newsroom/press-releases/2022-10-10-gartner-identifies-seven-technology-disruptions-that-willimpact-sales-through-2027.
[12] ZHANG Y.Narrative Illustration Creation of Folk Tale Theme[D].Hangzhou:Zhejiang Sci-Tech University,2019.
[13] LAWRENCE Z.What is Illustration Design?[M].China Youth Publishing House,2011.
[14] HE G M.A Review of Domestic Image Narrative Research Since the 21st Century(I)[J].Journal of Hubei Institute of Fine Arts,2018(4):49-58.
[15] LIU T,WU Y,WANG K Z.Automated Digest Review[J].Information Science,1998(1):65-71.
[16] GONG S.Research on Text Representation of Extractive Multi-Document Abstract[D].Beijing:Beijing Jiaotong University,2013.
[17] MOHAN M J,SUNITHA C,GANESH A,et al.A Study on Ontology Based Abstractive Summarization[J].Procedia Computer Science,2016,87:32-37.
[18] JIANG M.Research on Hand-drawn Illustration Creation Based on Emotional Design Theory[D].Wuhan:Hubei University of Technology,2021.
[19] TAN C P.Review of Fine-grained Sentiment Analysis of Text[J].Journal of University Libraries,2022,43(4):85-99,119.
[1] JIANG Rui, YANG Kaihui, WANG Xiaoming, LI Dapeng, XU Youyun. Attentional Interaction-based Deep Learning Model for Chinese Question Answering [J]. Computer Science, 2024, 51(6): 325-330.
[2] GUO Shangzhi, LIAO Xiaofeng, XIAN Kaiyi. Logical Regression Click Prediction Algorithm Based on Combination Structure [J]. Computer Science, 2024, 51(2): 73-78.
[3] RAO Yi, YUAN Bochuan, YUAN Yubo. Recognition Method of Online Classroom Interaction Based on Learner State [J]. Computer Science, 2024, 51(11A): 231200133-9.
[4] WANG Shuaiwei, LEI Jie, FENG Zunlei, LIANG Ronghua. Review of Visual Representation Learning [J]. Computer Science, 2024, 51(11): 112-132.
[5] YAO Tianlei, CHEN Xiliang, YU Peiyi. Review of Generative Reinforcement Learning Based on Sequence Modeling [J]. Computer Science, 2024, 51(11): 213-228.
[6] ZHANG Ce, CHU Dianhui, ZHANG Qiao, LIU Peng, WEI Meng, LIU Xiaoying. Metaverse Teaching:A Higher Form of Digital Teaching Transformation in Higher Education [J]. Computer Science, 2024, 51(10): 1-9.
[7] DING Weilong, LIU Jinlong, ZHU Wei, LIAO Wanyin. Review of Quality Control Algorithms for Pathological Slides Based on Deep Learning [J]. Computer Science, 2024, 51(10): 276-286.
[8] ZHANG Wenqiong, LI Yun. Fairness Metrics of Machine Learning:Review of Status,Challenges and Future Directions [J]. Computer Science, 2024, 51(1): 266-272.
[9] WANG Zibo, ZHANG Yaofang, CHEN Yilu, LIU Hongri, WANG Bailing, WANG Chonghua. Hierarchical Task Network Planning Based Attack Path Discovery [J]. Computer Science, 2023, 50(9): 35-43.
[10] ZHOU Fengfan, LING Hefei, ZHANG Jinyuan, XIA Ziwei, SHI Yuxuan, LI Ping. Facial Physical Adversarial Example Performance Prediction Algorithm Based on Multi-modal Feature Fusion [J]. Computer Science, 2023, 50(8): 280-285.
[11] HUANG Pei, LIU Minghao, MA Feifei, ZHANG Jian. Automated Reasoning Techniques for Solving Combinatorial Mathematical Problems:A Survey [J]. Computer Science, 2023, 50(7): 167-175.
[12] WANG Dongli, YANG Shan, OUYANG Wanli, LI Baopu, ZHOU Yan. Explainability of Artificial Intelligence:Development and Application [J]. Computer Science, 2023, 50(6A): 220600212-7.
[13] WANG Zihan, TONG Xiangrong. Research Progress of Multi-agent Path Finding Based on Conflict-based Search Algorithms [J]. Computer Science, 2023, 50(6): 358-368.
[14] XING Ying. Review of Software Engineering Techniques and Methods Based on Explainable Artificial Intelligence [J]. Computer Science, 2023, 50(5): 3-11.
[15] ZHANG Qiyang, CHEN Xiliang, CAO Lei, LAI Jun, SHENG Lei. Survey on Knowledge Transfer Method in Deep Reinforcement Learning [J]. Computer Science, 2023, 50(5): 201-216.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!