Intelligent interaction system for art image creation: challenges, methodologies, and future prospects

Huanghuang DENG; Aoran SUN; Zhan MENG; Yongwei WANG; Luyang CHEN; Dunhan LI; Zhiyu LI; Yue HAN; Zejian LI; Kun KUANG; Zemin LIU; Ning ZOU; Zhou ZHAO; Kejun ZHANG; Lingyun SUN; Yu ZHAO; Fei WU; Yunhe PAN

doi:10.1631/ENG.ITEE.2026.0124

Your Location：

Home >

Browse articles >

Intelligent interaction system for art image creation: challenges, methodologies, and future prospects

Regular Papers | Updated：2026-11-05

- Intelligent interaction system for art image creation: challenges, methodologies, and future prospects
- Intelligent interaction system for art image creation: challenges, methodologies, and future prospects
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 10, Pages: 1-28(2026)
- Affiliations：
  
  State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou 310027, China
  College of Artificial Intelligence, Zhejiang University, Hangzhou 310058, China
  School of Software Technology, Zhejiang University, Hangzhou 310058, China
  College of Media and International Culture, Zhejiang University, Hangzhou 310058, China
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  Institute of Intelligent Media and Film Arts, Zhejiang University, Hangzhou 310058, China
- Author bio：
  
  ✉ Yongwei WANG, yongwei.wang@zju.edu.cn
  ✉ Yu ZHAO, yuzhao@zju.edu.cn
  ✉ Fei WU, wufei@zju.edu.cn
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0124
  CLC： TP391.41
- Received：28 April 2026，
  
  Revised：2026-08-31，
  
  Published：01 October 2026
- Accepted：
Scan QR Code
Huanghuang DENG, Aoran SUN, Zhan MENG, et al. Intelligent interaction system for art image creation: challenges, methodologies, and future prospects[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(10): 1-28.
DOI：

Huanghuang DENG, Aoran SUN, Zhan MENG, et al. Intelligent interaction system for art image creation: challenges, methodologies, and future prospects[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(10): 1-28. DOI： 10.1631/ENG.ITEE.2026.0124.

摘要

Abstract

Graphic visual art is undergoing a profound transformation driven by generative artificial intelligence (AI). This transformation extends beyond technological innovation

reshaping the paradigms of artistic image creation. This paper examines human-centered interactive systems for artistic image creation

aiming to bridge the gap between technology-oriented and art-oriented research. We identify three core challenges in interactive generative art creation: controllability

integrity

and creativity

and analyze in depth the underlying trade-offs among them. Building on this analysis

we systematically review the evolution of generative models

approaches to integrating artistic knowledge

and the emergence of collaborative agentic AI systems. Looking ahead

we emphasize the need to develop human–AI collaborative frameworks that offer greater controllability

preserve human creative agency

and ensure regulatory compliance

while exploring a wide range of potential applications. This study proposes a unified analytical framework that connects AI technologies

human–AI interaction processes

and the integration and innovation of artistic image creation. It further outlines a task-oriented roadmap to guide future research and the development of collaborative systems for artistic image creation.

关键词

Keywords

references

Achlioptas P , Ovsjanikov M , Haydarov K , et al. , 2021 . ArtEmis: affective language for visual art . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 11564 - 11574 . https://doi.org/10.1109/CVPR46437.2021.01140 https://doi.org/10.1109/CVPR46437.2021.01140

Adamkiewicz K , Woźniak PW , Dominiak J , et al. , 2025 . PromptMap: an alternative interaction style for AI-based image generation . Proc 30 th Int Conf on Intelligent User Interfaces , p. 1162 - 1176 . https://doi.org/10.1145/3708359.3712150 https://doi.org/10.1145/3708359.3712150

Agudo U , Arrese M , Liberal KG , et al. , 2022 . Assessing emotion and sensitivity of AI artwork . Front Psychol , 13 : 879088 . https://doi.org/10.3389/fpsyg.2022.879088 https://doi.org/10.3389/fpsyg.2022.879088

AlDahoul N , Rahwan T , Zaki Y , 2025 . AI-generated faces influence gender stereotypes and racial homogenization . Sci Rep , 15 ( 1 ): 14449 . https://doi.org/10.1038/s41598-025-99623-3 https://doi.org/10.1038/s41598-025-99623-3

Amabile TM , 1982 . Social psychology of creativity: a consensual assessment technique . J Pers Soc Psychol , 43 ( 5 ): 997 - 1013 . https://doi.org/10.1037/0022-3514.43.5.997 https://doi.org/10.1037/0022-3514.43.5.997

Ambartsumian Y , Cannon MT , 2025 . Why the obsession with human creativity? A comparative analysis on copyright registration of AI-generated works . Harvard Int Law J .

Avakri A , Avakri I , 2025 . Analysis of the creative mode of intelligent image generation art . Proc Int Conf on Artificial Intelligence, Digital Media Technology and Interaction Design , p. 246 - 250 . https://doi.org/10.1145/3726010.3726048 https://doi.org/10.1145/3726010.3726048

Avrahami O , Lischinski D , Fried O , 2022 . Blended diffusion for text-driven editing of natural images . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 18187 - 18197 . https://doi.org/10.1109/CVPR52688.2022.01767 https://doi.org/10.1109/CVPR52688.2022.01767

Bahng H , Yoo S , Cho W , et al. , 2018 . Coloring with words: guiding image colorization through text-based palette generation . Proc 15 th European Conf on Computer Vision , p. 443 - 459 . https://doi.org/10.1007/978-3-030-01258-8_27 https://doi.org/10.1007/978-3-030-01258-8_27

Bau D , Zhu JY , Strobelt H , et al. , 2019 . GAN dissection: visualizing and understanding generative adversarial networks . Proc 7 th Int Conf on Learning Representations .

Bayramli Z , Suleymanzade A , An NM , et al. , 2025 . Diffusion models through a global lens: are they culturally inclusive ? Proc 63 rd Annual Meeting of the Association for Computational Linguistics , p. 31137 - 31155 . https://doi.org/10.18653/v1/2025.acl-long.1503 https://doi.org/10.18653/v1/2025.acl-long.1503

Blattmann A , Rombach R , Ling H , et al. , 2023 . Align your latents: high-resolution video synthesis with latent diffusion models . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 22563 - 22575 . https://doi.org/10.1109/CVPR52729.2023.02161 https://doi.org/10.1109/CVPR52729.2023.02161

Boden MA , 2004 . The Creative Mind: Myths and Mechanisms . 2 nd Edition . Routledge , London, UK . https://doi.org/10.4324/9780203508527 https://doi.org/10.4324/9780203508527

Brooks T , Holynski A , Efros AA , 2023 . InstructPix2Pix: learning to follow image editing instructions . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 18392 - 18402 . https://doi.org/10.1109/CVPR52729.2023.01764 https://doi.org/10.1109/CVPR52729.2023.01764

Buolamwini J , Gebru T , 2018 . Gender shades: intersectional accuracy disparities in commercial gender classification . Proc Conf on Fairness, Accountability and Transparency , p. 77 - 91 .

Cai A , Rick SR , Heyman JL , et al. , 2023 . DesignAID: using generative AI and semantic diversity for design inspiration . Proc ACM Collective Intelligence Conf , p. 1 - 11 . https://doi.org/10.1145/3582269.3615596 https://doi.org/10.1145/3582269.3615596

Canny J , 1986 . A computational approach to edge detection . IEEE Trans Patt Anal Mach Intell , PAMI-8 ( 6 ): 679 - 698 . https://doi.org/10.1109/TPAMI.1986.4767851 https://doi.org/10.1109/TPAMI.1986.4767851

Cao HQ , Tan C , Gao ZY , et al. , 2024 . A survey on generative diffusion models . IEEE Trans Knowl Data Eng , 36 ( 7 ): 2814 - 2830 . https://doi.org/10.1109/TKDE.2024.3361474 https://doi.org/10.1109/TKDE.2024.3361474

Carlini N , Hayes J , Nasr M , et al. , 2023 . Extracting training data from diffusion models . Proc 32 nd USENIX Conf on Security Symp , Article 294 .

Chamberlain R , Mullin C , Scheerlinck B , et al. , 2018 . Putting the art in artificial: aesthetic responses to computer-generated art . Psychol Aesthet Crea Arts , 12 ( 2 ): 177 - 192 . https://doi.org/10.1037/aca0000136 https://doi.org/10.1037/aca0000136

Chang HW , Zhang H , Jiang L , et al. , 2022 . MaskGIT: masked generative image transformer . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 11305 - 11315 . https://doi.org/10.1109/CVPR52688.2022.01103 https://doi.org/10.1109/CVPR52688.2022.01103

Chen C , Liu DC , Xu C , 2024 . Towards memorization-free diffusion models . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 8425 - 8434 . https://doi.org/10.1109/CVPR52733.2024.00805 https://doi.org/10.1109/CVPR52733.2024.00805

Chen LC , Zhu YK , Papandreou G , et al. , 2018 . Encoder-decoder with atrous separable convolution for semantic image segmentation . Proc 15 th European Conf on Computer Vision , p. 833 - 851 . https://doi.org/10.1007/978-3-030-01234-2_49 https://doi.org/10.1007/978-3-030-01234-2_49

Cheng JH , Yin BQ , Cai KX , et al. , 2024 . TheaterGen: character management with LLM for consistent multi-turn image generation . https://arxiv.org/abs/2404.18919 https://arxiv.org/abs/2404.18919

Chung JJY , Adar E , 2023 . PromptPaint: steering text-to-image generation through paint medium-like interactions . Proc 36 th Annual ACM Symp on User Interface Software and Technology , Article 6 . https://doi.org/10.1145/3586183.3606777 https://doi.org/10.1145/3586183.3606777

Coalition for Content Provenance and Authenticity , 2024 . C2PA Specifications . https://spec.c2pa.org/ https://spec.c2pa.org/ [Accessed on Aug. 4, 2026 ] .

Coalition for Content Provenance and Authenticity , 2026a . Content Credentials: C2PA Technical Specification . https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html [Accessed on Aug. 4, 2026 ] .

Coalition for Content Provenance and Authenticity , 2026b . Guiding Principles for C2PA Designs and Specifications . https://c2pa.org/principles/ https://c2pa.org/principles/ [Accessed on Aug. 4, 2026 ] .

Costa V , Alonso-Moral JM , Falomir Z , et al. , 2023 . An art painting style explainable classifier grounded on logical and commonsense reasoning . Soft Comput . https://doi.org/10.1007/s00500-023-08258-x https://doi.org/10.1007/s00500-023-08258-x

Cui P , Lü Z , Zhang YS , 2025 . Construction and design application of plant pattern cultural genealogy in Changsha kiln . Furnit Interior Des , 32 ( 10 ): 64 - 71 (in Chinese) . https://doi.org/10.16771/j.cn43-1247/ts.2025.10.010 https://doi.org/10.16771/j.cn43-1247/ts.2025.10.010

Cyberspace Administration of China , 2025 . Measures for Labelling Artificial-Intelligence-Generated and Synthetic Content (in Chinese) . https://www.cac.gov.cn/2025-03/14/c_1743654684782215.htm https://www.cac.gov.cn/2025-03/14/c_1743654684782215.htm [Accessed on Aug. 4, 2026 ] .

Dalal N , Triggs B , 2005 . Histograms of oriented gradients for human detection . Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition , p. 886 - 893 . https://doi.org/10.1109/CVPR.2005.177 https://doi.org/10.1109/CVPR.2005.177

Darda KM , Chatterjee A , 2023 . The impact of contextual information on aesthetic engagement of artworks . Sci Rep , 13 ( 1 ): 4273 . https://doi.org/10.1038/s41598-023-30768-9 https://doi.org/10.1038/s41598-023-30768-9

Demmer TR , Kühnapfel C , Fingerhut J , 2023 . Does an emotional connection to art really require a human artist? Emotion and intentionality responses to AI- versus human-created art and impact on aesthetic experience . Comput Hum Behav , 148 : 107875 . https://doi.org/10.1016/j.chb.2023.107875 https://doi.org/10.1016/j.chb.2023.107875

Desai DR , Riedl M , 2024 . Between copyright and computer science: the law and ethics of generative AI . https://arxiv.org/abs/2403.14653 https://arxiv.org/abs/2403.14653

Dhariwal P , Nichol A , 2021 . Diffusion models beat GANs on image synthesis . Proc 35 th Int Conf on Neural Information Processing Systems , Article 672 .

D'Incà M , Peruzzo E , Mancini M , et al. , 2024 . OpenBias: open-set bias detection in text-to-image generative models . https://arxiv.org/abs/2404.07990 https://arxiv.org/abs/2404.07990

Ding X , Wang YW , Zhang K , et al. , 2026 . CCDM: continuous conditional diffusion models for image generation . IEEE Trans Multimedia , 28 : 4360 - 4372 . https://doi.org/10.1109/TMM.2026.3660128 https://doi.org/10.1109/TMM.2026.3660128

Esser P , Rombach R , Ommer B , 2021 . Taming transformers for high-resolution image synthesis . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 12868 - 12878 . https://doi.org/10.1109/CVPR46437.2021.01268 https://doi.org/10.1109/CVPR46437.2021.01268

European Commission , 2025 . Explanatory Notice and Template for the Public Summary of Training Content for General-Purpose AI Models . https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models [Accessed on Aug. 4, 2026 ] .

European Union , 2024 . Regulation (EU) 2024/1689 Laying Down Harmonised Rules on Artificial Intelligence . https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:?32024R1689 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:?32024R1689 [Accessed on Aug. 4, 2026 ] .

Frich J , MacDonald Vermeulen L , Remy C , et al. , 2019 . Mapping the landscape of creativity support tools in HCI . Proc CHI Conf on Human Factors in Computing Systems , Article 389 . https://doi.org/10.1145/3290605.3300619 https://doi.org/10.1145/3290605.3300619

Garcia N , Renoust B , Nakashima Y , 2019 . Context-aware embeddings for automatic art analysis . Proc ACM Int Conf on Multimedia Retrieval , p. 25 - 33 . https://doi.org/10.1145/3323873.3325028 https://doi.org/10.1145/3323873.3325028

Goodfellow IJ , Pouget-Abadie J , Mirza M , et al. , 2014 . Generative adversarial nets . Proc 28 th Int Conf on Neural Information Processing Systems , p. 2672 - 2680 .

Gu A , Dao T , 2024 . Mamba: linear-time sequence modeling with selective state spaces . Proc 1 st Conf on Language Modeling .

Han SJ , Fussell SR , 2025 . Understanding user perceptions and the role of AI image generators in image creation workflows . Proc CHI Conf on Human Factors in Computing Systems , Article 547 . https://doi.org/10.1145/3706598.3713227 https://doi.org/10.1145/3706598.3713227

Härkönen E , Hertzmann A , Lehtinen J , et al. , 2020 . GANSpace: discovering interpretable GAN controls . Proc 34 th Int Conf on Neural Information Processing Systems , Article 825 .

He BX , Guo Y , Zhu YB , et al. , 2024 . From signal to knowledge: the diagnostic value of raw data in the artificial intelligence prediction of human data for the first time . Engineering , 34 : 60 - 69 . https://doi.org/10.1016/j.eng.2023.02.013 https://doi.org/10.1016/j.eng.2023.02.013

Hertz A , Mokady R , Tenenbaum J , et al. , 2022 . Prompt-to-prompt image editing with cross attention control . https://arxiv.org/abs/2208.01626 https://arxiv.org/abs/2208.01626

Hessel J , Holtzman A , Forbes M , et al. , 2021 . CLIPScore: a reference-free evaluation metric for image captioning . Proc Conf on Empirical Methods in Natural Language Processing , p. 7514 - 7528 . https://doi.org/10.18653/v1/2021.emnlp-main.595 https://doi.org/10.18653/v1/2021.emnlp-main.595

Heusel M , Ramsauer H , Unterthiner T , et al. , 2017 . GANs trained by a two time-scale update rule converge to a local Nash equilibrium . Proc 31 st Int Conf on Neural Information Processing Systems , p. 6629 - 6640 .

Higgins I , Matthey L , Pal A , et al. , 2017 . β -VAE: learning basic visual concepts with a constrained variational framework . Proc 5 th Int Conf on Learning Representations .

Ho J , Salimans T , 2022 . Classifier-free diffusion guidance . https://arxiv.org/abs/2207.12598 https://arxiv.org/abs/2207.12598

Ho J , Jain A , Abbeel P , 2020 . Denoising diffusion probabilistic models . Proc 34 th Int Conf on Neural Information Processing Systems , Article 574 .

Hu MH , Zheng JB , Liu DQ , et al. , 2023 . Cocktail: mixing multi-modality controls for text-conditional image generation . https://arxiv.org/abs/2306.00964 https://arxiv.org/abs/2306.00964

Huang LH , Chen D , Liu Y , et al. , 2023 . Composer: creative and controllable image synthesis with composable conditions . Proc 40 th Int Conf on Machine Learning , p. 13753 - 13773 .

Huang YK , Ning Z , Cheng M , 2025 . LACE: exploring turn-taking and parallel interaction modes in human-AI co-creation for iterative image generation . https://arxiv.org/abs/2504.14827 https://arxiv.org/abs/2504.14827

Isola P , Zhu JY , Zhou TH , et al. , 2017 . Image-to-image translation with conditional adversarial networks . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 5967 - 5976 . https://doi.org/10.1109/CVPR.2017.632 https://doi.org/10.1109/CVPR.2017.632

Jeong S , Choi I , Yun Y , et al. , 2025 . Culture-TRIP: culturally-aware text-to-image generation with iterative prompt refinement . Proc Conf of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies , p. 9543 - 9573 . https://doi.org/10.18653/v1/2025.naacl-long.483 https://doi.org/10.18653/v1/2025.naacl-long.483

Jing PG , Liu XY , Wang J , 2023 . StyleEDL: style-guided high-order attention network for image emotion distribution learning . Proc 31 st ACM Int Conf on Multimedia , p. 853 - 861 . https://doi.org/10.1145/3581783.3612040 https://doi.org/10.1145/3581783.3612040

Ju X , Zeng AL , Zhao CC , et al. , 2023 . HumanSD: a native skeleton-guided diffusion model for human image generation . Proc IEEE/CVF Int Conf on Computer Vision , p. 15942 - 15952 . https://doi.org/10.1109/ICCV51070.2023.01465 https://doi.org/10.1109/ICCV51070.2023.01465

Ju X , Liu X , Wang XT , et al. , 2024 . BrushNet: a plug-and-play image inpainting model with decomposed dual-branch diffusion . Proc 18 th European Conf on Computer Vision , p. 150 - 168 . https://doi.org/10.1007/978-3-031-72661-3_9 https://doi.org/10.1007/978-3-031-72661-3_9

Jucker JL , Barrett JL , Wlodarski R , 2014 . “I just don’t get it”: perceived artists’ intentions affect art evaluations . Empir Stud Arts , 32 ( 2 ): 149 - 182 . https://doi.org/10.2190/EM.32.2.c https://doi.org/10.2190/EM.32.2.c

Karras T , Laine S , Aila T , 2019 . A style-based generator architecture for generative adversarial networks . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 4396 - 4405 . https://doi.org/10.1109/CVPR.2019.00453 https://doi.org/10.1109/CVPR.2019.00453

Karras T , Laine S , Aittala M , et al. , 2020 . Analyzing and improving the image quality of StyleGAN . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 8107 - 8116 . https://doi.org/10.1109/CVPR42600.2020.00813 https://doi.org/10.1109/CVPR42600.2020.00813

Kirstain Y , Polyak A , Singer U , et al. , 2023 . Pick-a-Pic: an open dataset of user preferences for text-to-image generation . Proc 37 th Int Conf on Neural Information Processing Systems , Article 1594 .

Klassert T , Ulges A , Fu BY , 2026 . BAFIS: dataset + framework to assess occupational bias and human preference in modern text-to-image models . Proc IEEE/CVF Winter Conf on Applications of Computer Vision , p. 2168 - 2177 . https://doi.org/10.1109/WACV61042.2026.00215 https://doi.org/10.1109/WACV61042.2026.00215

Lan XY , An JX , Guo YS , et al. , 2025 . Imagining the Far East: exploring perceived biases in AI-generated images of East Asian women . Proc CHI Conf on Human Factors in Computing Systems , Article 332 . https://doi.org/10.1145/3706599.3719678 https://doi.org/10.1145/3706599.3719678

Leder H , Belke B , Oeberst A , et al. , 2004 . A model of aesthetic appreciation and aesthetic judgments . Brit J Psychol , 95 ( 4 ): 489 - 508 . https://doi.org/10.1348/0007126042369811 https://doi.org/10.1348/0007126042369811

Li YH , Liu HT , Wu QY , et al. , 2023 . GLIGEN: open-set grounded text-to-image generation . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 22511 - 22521 . https://doi.org/10.1109/CVPR52729.2023.02156 https://doi.org/10.1109/CVPR52729.2023.02156

Li YT , Gan Z , Shen YL , et al. , 2019 . StoryGAN: a sequential conditional GAN for story visualization . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 6322 - 6331 . https://doi.org/10.1109/CVPR.2019.00649 https://doi.org/10.1109/CVPR.2019.00649

Li ZJ , Zhang Y , Zhou SZ , et al. , 2025 . RealtimeGen: an intervenable AI image generation system for commercial digital art asset creators . Int J Hum–Comput Interact , 41 ( 11 ): 6613 - 6636 . https://doi.org/10.1080/10447318.2024.2382508 https://doi.org/10.1080/10447318.2024.2382508

Liu QQ , Yan YP , Jin YC , et al. , 2024 . Secure federated evolutionary optimization—a survey . Engineering , 34 : 23 - 42 . https://doi.org/10.1016/j.eng.2023.10.006 https://doi.org/10.1016/j.eng.2023.10.006

Long J , Shelhamer E , Darrell T , 2015 . Fully convolutional networks for semantic segmentation . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 3431 - 3440 . https://doi.org/10.1109/CVPR.2015.7298965 https://doi.org/10.1109/CVPR.2015.7298965

Lv KT , Chen HB , Yuan YJ , et al. , 2025 . How to inject knowledge efficiently? Knowledge infusion scaling law for pre-training large language models . Proc Conf on Empirical Methods in Natural Language Processing , p. 26193 - 26208 . https://doi.org/10.18653/v1/2025.emnlp-main.1331 https://doi.org/10.18653/v1/2025.emnlp-main.1331

Ma Z , Li QM , Zhang XH , et al. , 2025 . An inversion-based measure of memorization for diffusion models . Proc IEEE/CVF Int Conf on Computer Vision , p. 16959 - 16969 . https://doi.org/10.1109/ICCV51701.2025.01575 https://doi.org/10.1109/ICCV51701.2025.01575

Machajdik J , Hanbury A , 2010 . Affective image classification using features inspired by psychology and art theory . Proc 18 th ACM Int Conf on Multimedia , p. 83 - 92 . https://doi.org/10.1145/1873951.1873965 https://doi.org/10.1145/1873951.1873965

Maher ML , Grace K , Karimi P , et al. , 2018 . Evaluating creativity in computational co-creative systems . Proc 9 th Int Conf on Computational Creativity , p. 104 - 111 .

Man F , Chen X , Wang H , et al. , 2025 . K-EVER2: knowledge-enhanced visual emotion reasoning and retrieval . Proc 33 rd ACM Int Conf on Multimedia .

Matsumoto H , Nakamura A , Hamaguchi K , et al. , 2025 . From sketch to refinement: interactive drawing support with generative AI . Proc 14 th Global Conf on Consumer Electronics , p. 32 - 36 . https://doi.org/10.1109/GCCE65946.2025.11274974 https://doi.org/10.1109/GCCE65946.2025.11274974

Millet K , Buehler F , Du GZ , et al. , 2023 . Defending humankind: anthropocentric bias in the appreciation of AI art . Comput Hum Behav , 143 : 107707 . https://doi.org/10.1016/j.chb.2023.107707 https://doi.org/10.1016/j.chb.2023.107707

Mokady R , Hertz A , Aberman K , et al. , 2023 . Null-text inversion for editing real images using guided diffusion models . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 6038 - 6047 . https://doi.org/10.1109/CVPR52729.2023.00585 https://doi.org/10.1109/CVPR52729.2023.00585

Mou C , Wang XT , Xie LB , et al. , 2024 . T2I-Adapter: learning adapters to dig out more controllable ability for text-to-image diffusion models . Proc 38 th AAAI Conf on Artificial Intelligence , p. 4296 - 4304 . https://doi.org/10.1609/aaai.v38i5.28226 https://doi.org/10.1609/aaai.v38i5.28226

Nayak S , Bhatia M , Zhang XF , et al. , 2025 . CulturalFrames: assessing cultural expectation alignment in text-to-image models and evaluation metrics . Proc Association for Computational Linguistics , p. 20918 - 20953 .

Nazeri K , Ng E , Joseph T , et al. , 2019 . EdgeConnect: generative image inpainting with adversarial edge learning . https://arxiv.org/abs/1901.00212 https://arxiv.org/abs/1901.00212

O'Donovan P , Agarwala A , Hertzmann A , 2011 . Color compatibility from large datasets . ACM Trans Graph , 30 ( 4 ): 43 . https://doi.org/10.1145/2010324.1964958 https://doi.org/10.1145/2010324.1964958

Ojala T , Pietikainen M , Mäenpää T , 2002 . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns . IEEE Trans Patt Anal Mach Intell , 24 ( 7 ): 971 - 987 . https://doi.org/10.1109/TPAMI.2002.1017623 https://doi.org/10.1109/TPAMI.2002.1017623

Oppenlaender J , Johnston H , Silvennoinen JM , et al. , 2025 . Artworks reimagined: exploring human-AI co-creation through body prompting . Proc ACM Hum-Comput Interact , 9 ( 4 ): EICS012 . https://doi.org/10.1145/3734189 https://doi.org/10.1145/3734189

O'Quin K , Besemer SP , 1989 . The development, reliability, and validity of the revised creative product semantic scale . Creativ Res J , 2 ( 4 ): 267 - 278 . https://doi.org/10.1080/10400418909534323 https://doi.org/10.1080/10400418909534323

Pan XG , Tewari A , Leimkühler T , et al. , 2023 . Drag your GAN: interactive point-based manipulation on the generative image manifold . Proc ACM SIGGRAPH Conf , Article 78 . https://doi.org/10.1145/3588432.3591500 https://doi.org/10.1145/3588432.3591500

Pan YH , 2019 . On visual knowledge . Front Inform Technol Electron Eng , 20 ( 8 ): 1021 - 1025 . https://doi.org/10.1631/FITEE.1910001 https://doi.org/10.1631/FITEE.1910001

Pan YH , 2020 . Multiple knowledge representation of artificial intelligence . Engineering , 6 ( 3 ): 216 - 217 . https://doi.org/10.1016/j.eng.2019.12.011 https://doi.org/10.1016/j.eng.2019.12.011

Park T , Liu MY , Wang TC , et al. , 2019 . Semantic image synthesis with spatially-adaptive normalization . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 2332 - 2341 . https://doi.org/10.1109/CVPR.2019.00244 https://doi.org/10.1109/CVPR.2019.00244

Parmar G , Singh KK , Zhang R , 2023 . Zero-shot image-to-image translation . Proc ACM SIGGRAPH Conf , Article 11 . https://doi.org/10.1145/3588432.3591513 https://doi.org/10.1145/3588432.3591513

Peng YJ , Han JH , Zhang ZL , et al. , 2024 . The Tong test: evaluating artificial general intelligence through dynamic embodied physical and social interactions . Engineering , 34 : 12 - 22 . https://doi.org/10.1016/j.eng.2023.07.006 https://doi.org/10.1016/j.eng.2023.07.006

Qadri R , Mirowski P , Gabriellan A , et al. , 2024 . Dialogue with the machine and dialogue with the art world: evaluating generative AI for culturally-situated creativity . https://arxiv.org/abs/2412.14077 https://arxiv.org/abs/2412.14077

Radford A , Kim JW , Hallacy C , et al. , 2021 . Learning transferable visual models from natural language supervision . Proc 38 th Int Conf on Machine Learning , p. 8748 - 8763 .

Rafner JF , Zana B , Hansen IB , et al. , 2025 . Agency in human-AI collaboration for image generation and creative writing: preliminary insights from think-aloud protocols . Creativ Res J . https://doi.org/10.1080/10400419.2025.2587803 https://doi.org/10.1080/10400419.2025.2587803

Ramesh A , Pavlov M , Goh G , et al. , 2021 . Zero-shot text-to-image generation . Proc 38 th Int Conf on Machine Learning , p. 8821 - 8831 .

Ramesh A , Dhariwal P , Nichol A , et al. , 2022 . Hierarchical text-conditional image generation with CLIP latents . https://arxiv.org/abs/2204.06125 https://arxiv.org/abs/2204.06125

Ranftl R , Bochkovskiy A , Koltun V , 2021 . Vision transformers for dense prediction . Proc IEEE/CVF Int Conf on Computer Vision , p. 12159 - 12168 . https://doi.org/10.1109/ICCV48922.2021.01196 https://doi.org/10.1109/ICCV48922.2021.01196

Rezwana J , Maher ML , 2023 . Designing creative AI partners with COFI: a framework for modeling interaction in human-AI co-creative systems . ACM Trans Computer-Human Interact , 30 ( 5 ): 67 . https://doi.org/10.1145/3519026 https://doi.org/10.1145/3519026

Rhodes M , 1961 . An analysis of creativity . Phi Delta Kappan , 42 ( 7 ): 305 - 310 .

Rombach R , Blattmann A , Lorenz D , et al. , 2022 . High-resolution image synthesis with latent diffusion models . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 10674 - 10685 . https://doi.org/10.1109/CVPR52688.2022.01042 https://doi.org/10.1109/CVPR52688.2022.01042

Royal Courts of Justice , 2025 . In the High Court of Justice Business and Property Courts of England and Wales Intellectual Property List (ChD) . https://www.judiciary.uk/wp-content/uploads/2025/11/Getty-Images-v-Stability-AI.pdf https://www.judiciary.uk/wp-content/uploads/2025/11/Getty-Images-v-Stability-AI.pdf [Accessed on Aug. 4, 2026 ] .

Ruiz N , Li YZ , Jampani V , et al. , 2023 . DreamBooth: fine tuning text-to-image diffusion models for subject-driven generation . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 22500 - 22510 . https://doi.org/10.1109/CVPR52729.2023.02155 https://doi.org/10.1109/CVPR52729.2023.02155

Runco MA , Jaeger GJ , 2012 . The standard definition of creativity . Creativ Res J , 24 ( 1 ): 92 - 96 . https://doi.org/10.1080/10400419.2012.650092 https://doi.org/10.1080/10400419.2012.650092

Saharia C , Chan W , Chang HW , et al. , 2022a . Palette: Image-to-image diffusion models . Proc ACM SIGGRAPH Conf , Article 15 . https://doi.org/10.1145/3528233.3530757 https://doi.org/10.1145/3528233.3530757

Saharia C , Chan W , Saxena S , et al. , 2022b . Photorealistic text-to-image diffusion models with deep language understanding . Proc 36 th Int Conf on Neural Information Processing Systems , Article 2643 .

Satkunarajan J , Koch S , Kurzhals K , 2025 . The joy of co-painting: creative human-AI collaboration for traceable image-generation workflows . Proc 18 th Pacific Visualization Conf , p. 318 - 328 . https://doi.org/10.1109/PacificVis64226.2025.00038 https://doi.org/10.1109/PacificVis64226.2025.00038

Sengar SS , Hasan AB , Kumar S , et al. , 2025 . Generative artificial intelligence: a systematic review and applications . Multimed Tools Appl , 84 ( 21 ): 23661 - 23700 . https://doi.org/10.1007/s11042-024-20016-1 https://doi.org/10.1007/s11042-024-20016-1

Seshadri P , Singh S , Elazar Y , 2024 . The bias amplification paradox in text-to-image generation . Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , p. 6367 - 6384 . https://doi.org/10.18653/v1/2024.naacl-long.353 https://doi.org/10.18653/v1/2024.naacl-long.353

Shahriar S , 2022 . Gan computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network . Displays , 73 : 102237 . https://doi.org/10.1016/j.displa.2022.102237 https://doi.org/10.1016/j.displa.2022.102237

Shan S , Cryan J , Wenger E , et al. , 2023 . Glaze: protecting artists from style mimicry by text-to-image models . https://arxiv.org/abs/2302.04222 https://arxiv.org/abs/2302.04222

Shen YJ , Gu JJ , Tang XO , et al. , 2020 . Interpreting the latent space of GANs for semantic face editing . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 9240 - 9249 . https://doi.org/10.1109/CVPR42600.2020.00926 https://doi.org/10.1109/CVPR42600.2020.00926

Shi HY , Wang L , Zheng NN , et al. , 2022 . Loss functions for pose guided person image generation . Patt Recognit , 122 : 108351 . https://doi.org/10.1016/j.patcog.2021.108351 https://doi.org/10.1016/j.patcog.2021.108351

Shi WY , Upadhyay S , Quek G , et al. , 2025 . TalkSketch: multimodal generative AI for real-time sketch ideation with speech . https://arxiv.org/abs/2511.05817 https://arxiv.org/abs/2511.05817

Shneiderman B , 2022 . Human-Centered AI . Oxford University Press , Oxford, UK . https://doi.org/10.1093/oso/9780192845290.001.0001 https://doi.org/10.1093/oso/9780192845290.001.0001

Song Y , Sohl-Dickstein J , Kingma DP , 2021 . Score-based generative modeling through stochastic differential equations . Proc 9 th Int Conf on Learning Representations .

Song ZR , Yan B , Liu YH , et al. , 2025 . Injecting domain-specific knowledge into large language models: a comprehensive survey . Proc Association for Computational Linguistics , p. 25297 - 25311 .

Stein G , Cresswell JC , Hosseinzadeh R , et al. , 2023 . Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models . Proc 37 th Int Conf on Neural Information Processing Systems , Article 165 .

Stober S , Dornis TW , 2025 . Generative AI training and copyright law . https://arxiv.org/abs/2502.15858 https://arxiv.org/abs/2502.15858

Tan WR , Chan CS , Aguirre HE , et al. , 2019 . Improved ArtGAN for conditional synthesis of natural image and artwork . IEEE Trans Image Process , 28 ( 1 ): 394 - 409 . https://doi.org/10.1109/TIP.2018.2866698 https://doi.org/10.1109/TIP.2018.2866698

Tan ZT , Chai ML , Chen DD , et al. , 2021 . Diverse semantic image synthesis via probability distribution modeling . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 7958 - 7967 . https://doi.org/10.1109/CVPR46437.2021.00787 https://doi.org/10.1109/CVPR46437.2021.00787

Tao M , Tang H , Wu F , et al. , 2020 . DF-GAN: a simple and effective baseline for text-to-image synthesis . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 16494 - 16504 . https://doi.org/10.1109/CVPR52688.2022.01602 https://doi.org/10.1109/CVPR52688.2022.01602

Tholander J , Jonsson M , 2023 . Design ideation with AI - sketching, thinking and talking with generative machine learning models . Proc ACM Designing Interactive Systems Conf , p. 1930 - 1940 . https://doi.org/10.1145/3563657.3596014 https://doi.org/10.1145/3563657.3596014

UNESCO , 2003 . Text of the Convention for the Safeguarding of the Intangible Cultural Heritage . https://ich.unesco.org/en/convention https://ich.unesco.org/en/convention [Accessed on Aug. 4, 2026 ] .

UNESCO , 2021 . Recommendation on the Ethics of Artificial Intelligence . https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence [Accessed on Aug. 4, 2026 ] .

U.S. Copyright Office , 2025a . Copyright and Artificial Intelligence, Part 2: Copyrightability . U.S. Copyright Office , Washington, DC, USA .

U.S. Copyright Office , 2025b . Copyright and Artificial Intelligence, Part 3: Generative AI Training . U.S. Copyright Office , Washington, DC, USA .

Ungless E , Ross B , Lauscher A , 2023 . Stereotypes and smut: the (mis)representation of non-cisgender identities by text-to-image models . Proc Association for Computational Linguistics , p. 7919 - 7942 . https://doi.org/10.18653/v1/2023.findings-acl.502 https://doi.org/10.18653/v1/2023.findings-acl.502

United States Court of Appeals for the District of Columbia Circuit , 2025 . Thaler v. Perlmutter (D.C. Cir. 2025) . https://law.justia.com/cases/federal/appellate-courts/cadc/23-5233/23-5233-2025-03-18.html https://law.justia.com/cases/federal/appellate-courts/cadc/23-5233/23-5233-2025-03-18.html [Accessed on Aug. 4, 2026 ] .

United States District Court for the Northern District of California , 2026 . 23-201 - Andersen et al. v. Stability AI Ltd. et al . https://www.govinfo.gov/app/details/USCOURTS-cand-3_23-cv-00201 https://www.govinfo.gov/app/details/USCOURTS-cand-3_23-cv-00201 [Accessed on Aug. 4, 2026 ] .

van den Oord A , Vinyals O , Kavukcuoglu K , 2017 . Neural discrete representation learning . Proc 31 st Int Conf on Neural Information Processing Systems , p. 6309 - 6318 .

Vice J , Akhtar N , Sigal L , et al. , 2026 . On the fairness, diversity and reliability of text-to-image generative models . Artif Intell Rev , 59 : 57 . https://doi.org/10.1007/s10462-025-11424-2 https://doi.org/10.1007/s10462-025-11424-2

von Gioi RG , Jakubowicz J , Morel JM , et al. , 2010 . LSD: a fast line segment detector with a false detection control . IEEE Trans Patt Anal Mach Intell , 32 ( 4 ): 722 - 732 .

Wadinambiarachchi S , Kelly RM , Pareek S , et al. , 2024 . The effects of generative AI on design fixation and divergent thinking . Proc CHI Conf on Human Factors in Computing Systems , Article 380 . https://doi.org/10.1145/3613904.3642919 https://doi.org/10.1145/3613904.3642919

Wan YX , Wu D , Wang HR , et al. , 2024a . The factuality tax of diversity-intervened text-to-image generation: benchmark and fact-augmented intervention . Proc Conf on Empirical Methods in Natural Language Processing , p. 9082 - 9100 .

Wan YX , Subramonian A , Ovalle A , et al. , 2024b . Survey of bias in text-to-image generation: definition, evaluation, and mitigation . https://arxiv.org/abs/2404.01030 https://arxiv.org/abs/2404.01030

Wang BY , Chen QF , Wang ZY , 2025 . Diffusion-based visual art creation: a survey and new perspectives . ACM Comput Surv , 57 ( 10 ): 268 . https://doi.org/10.1145/3728459 https://doi.org/10.1145/3728459

Wang HF , Xing P , Huang RY , et al. , 2024 . InstantStyle-Plus: style transfer with content-preserving in text-to-image generation . https://arxiv.org/abs/2407.00788 https://arxiv.org/abs/2407.00788

Wang P , Zhang X , Wei LY , et al. , 2026 . Human-AI co-ideation via combinational generative model . J Eng Des , 37 ( 2 ): 458 - 494 . https://doi.org/10.1080/09544828.2025.2504309 https://doi.org/10.1080/09544828.2025.2504309

Wang RC , Zhang JL , Xie QS , et al. , 2024 . PainterNet: adaptive image inpainting with actual-token attention and diverse mask control . https://arxiv.org/abs/2412.01223 https://arxiv.org/abs/2412.01223

Wang YW , Shen T , Zhang SY , et al. , 2024 . Advances in edge-cloud collaboration and evolution for large-small models . J Image Graph , 29 ( 6 ): 1510 - 1534 (in Chinese) . https://doi.org/10.11834/jig.240011 https://doi.org/10.11834/jig.240011

Wang ZY , Li AX , Li ZG , et al. , 2024 . GenArtist: multimodal LLM as an agent for unified image generation and editing . Proc 38 th Int Conf on Neural Information Processing Systems .

Wortsman M , Ilharco G , Gadre SY , et al. , 2022 . Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time . Proc 39 th Int Conf on Machine Learning , p. 23965 - 23998 .

Wu F , Shen T , Bäck T , et al. , 2025 . Knowledge-empowered, collaborative, and co-evolving AI models: the post-LLM roadmap . Engineering , 44 : 87 - 100 . https://doi.org/10.1016/j.eng.2024.12.008 https://doi.org/10.1016/j.eng.2024.12.008

Wu JZ , Ge YX , Wang XT , et al. , 2023 . Tune-A-Video: one-shot tuning of image diffusion models for text-to-video generation . Proc IEEE/CVF Int Conf on Computer Vision , p. 7589 - 7599 . https://doi.org/10.1109/ICCV51070.2023.00701 https://doi.org/10.1109/ICCV51070.2023.00701

Xu JF , Liu ZY , Wang SL , et al. , 2023 . Foundations and applications of information systems dynamics . Engineering , 27 : 254 - 265 . https://doi.org/10.1016/j.eng.2022.04.018 https://doi.org/10.1016/j.eng.2022.04.018

Xu T , Zhang PC , Huang QY , et al. , 2018 . AttnGAN: fine-grained text to image generation with attentional generative adversarial networks . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 1316 - 1324 . https://doi.org/10.1109/CVPR.2018.00143 https://doi.org/10.1109/CVPR.2018.00143

Xu ZY , Duan KS , Shen XL , et al. , 2025 . PixelHacker: image inpainting with structural and semantic consistency . https://arxiv.org/abs/2504.20438 https://arxiv.org/abs/2504.20438

Yang BX , Gu SY , Zhang B , et al. , 2023 . Paint by Example: exemplar-based image editing with diffusion models . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 18381 - 18391 . https://doi.org/10.1109/CVPR52729.2023.01763 https://doi.org/10.1109/CVPR52729.2023.01763

Yang JY , Huang QR , Ding TT , 2023 . EmoSet: a large-scale visual emotion dataset with rich attributes . Proc IEEE/CVF Int Conf on Computer Vision , p. 20326 - 20337 . https://doi.org/10.1109/ICCV51070.2023.01864 https://doi.org/10.1109/ICCV51070.2023.01864

Yang JY , Feng JW , Huang H , 2024 . EmoGen: emotional image content generation with text-to-image diffusion models . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 6358 - 6368 . https://doi.org/10.1109/CVPR52733.2024.00608 https://doi.org/10.1109/CVPR52733.2024.00608

Yang ZY , Wang JF , Li LJ , et al. , 2024 . Idea2Img: iterative self-refinement with GPT-4V(ision) for automatic image design and generation . https://arxiv.org/abs/2310.08541 https://arxiv.org/abs/2310.08541

Zeiler MD , Fergus R , 2014 . Visualizing and understanding convolutional networks . Proc 13 th European Conf on Computer Vision , p. 818 - 833 . https://doi.org/10.1007/978-3-319-10590-1_53 https://doi.org/10.1007/978-3-319-10590-1_53

Zhang JS , Li K , Lai YK , et al. , 2021 . PISE: person image synthesis and editing with decoupled GAN . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 7978 - 7986 . https://doi.org/10.1109/CVPR46437.2021.00789 https://doi.org/10.1109/CVPR46437.2021.00789

Zhang L , Rao AY , Agrawala M , 2023 . Adding conditional control to text-to-image diffusion models . Proc IEEE/CVF Int Conf on Computer Vision , p. 3813 - 3824 . https://doi.org/10.1109/ICCV51070.2023.00355 https://doi.org/10.1109/ICCV51070.2023.00355

Zhang SF , Huang JF , Zhou Q , et al. , 2024 . Continuous-multiple image outpainting in one-step via positional query and a diffusion-based approach . Proc 12 th Int Conf on Learning Representations .

Zhang TY , Xie XX , Du XS , et al. , 2024 . Sketch-guided scene image generation . https://arxiv.org/abs/2407.06469 https://arxiv.org/abs/2407.06469

Zhao SH , Chen DD , Chen YC , et al. , 2023 . Uni-ControlNet: all-in-one control to text-to-image diffusion models . Proc 37 th Int Conf on Neural Information Processing Systems , Article 491 . https://doi.org/10.52202/075280-0491 https://doi.org/10.52202/075280-0491

Zhao SY , Cui J , Sheng YL , et al. , 2021 . Large scale image completion via co-modulated generative adversarial networks . Proc 9 th Int Conf on Learning Representations .

Zheng HZ , 2025 . Translation methods of AIGC in graphic creativity of intangible cultural heritage and art design strategies . East China Pulp Pap Ind , 55 ( 10 ): 82 - 84 (in Chinese) .

Zheng QH , Liu H , Zhang XQ , et al. , 2025 . Machine memory intelligence: inspired by human memory mechanisms . Engineering , 55 : 24 - 35 . https://doi.org/10.1016/j.eng.2025.01.012 https://doi.org/10.1016/j.eng.2025.01.012

Zhu JY , Park T , Isola P , et al. , 2017 . Unpaired image-to-image translation using cycle-consistent adversarial networks . Proc IEEE Int Conf on Computer Vision , p. 2242 - 2251 . https://doi.org/10.1109/ICCV.2017.244 https://doi.org/10.1109/ICCV.2017.244

Zhu RJ , Zhao QH , Li GQ , et al. , 2024 . SpikeGPT: generative pre-trained language model with spiking neural networks . https://arxiv.org/abs/2302.13939 https://arxiv.org/abs/2302.13939

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

A review of design intelligence: progress, problems, and challenges

A comprehensive survey of data classification based on evidence theory

SigmaGait: silhouette-guided motion-augmentation module for depth-based gait recognition

Stacked intelligent metasurface-aided multi-static sensing and cooperative communication

Expected graph Hilbert transform for time–frequency analysis of uncertain graph topologies

Related Author

Yong-chuan TANG

Jiang-jie HUANG

Meng-ting YAO

Jia WEI

Wei LI

Yong-xing HE

Ze-jian LI

Yifeng Li

Related Institution

College of Computer Science and Technology, Zhejiang University

Key Laboratory of Design Intelligence and Digital Creativity of Zhejiang Province

Hangzhou

Alibaba-Zhejiang University Joint Institute of Frontier Technologies

National Center for Applied Mathematics in Chongqing, Chongqing Normal University

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰