Diffusion-Based Visual Art Creation: A Survey and New Perspectives
Open accessAbstract
The integration of generative AI in visual art has revolutionized not only how visual content is created but also how AI interacts with and reflects the underlying domain knowledge. This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. We structure the survey into three phases: data feature and framework identification, detailed analyses using a structured coding process, and open-ended prospective outlooks. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We also provide insights into future directions from technical and synergistic perspectives, suggesting that the confluence of generative AI and art has shifted the creative paradigm and opened up new possibilities. By summarizing the development and trends of this emerging interdisciplinary area, we aim to shed light on the mechanisms through which AI systems emulate and, possibly, enhance human capacities in artistic perception and creativity.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cross-disciplinary application of AIGC tools showed particularly strong results in collaborative learning environments, with a 37% increase in interdisciplinary project outcomes compared to traditional approaches.
“The cross-disciplinary application of AIGC tools showed particularly strong results in collaborative learning environments, with a 37% increase in interdisciplinary project outcomes compared to traditional approaches40.”
From Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes (2025) · cites this paper as [40]
Section 3.1.3 mentions a review of AIGC in education, and Section 3.2.3 discusses integrating art into science education. Neither reports results for AIGC tools in collaborative learning or a 37% increase in interdisciplinary project outcomes. Searches for “37%” and interdisciplinary project outcomes found no matching result in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measuredOur 17.2% improvement with AIGC tools suggests superior efficacy compared to conventional learning management systems and multimedia tools, though the gap narrows when comparing to adaptive learning platforms (15% improvement).
“Our 17.2% improvement with AIGC tools suggests superior efficacy compared to conventional learning management systems and multimedia tools, though the gap narrows when comparing to adaptive learning platforms (15% improvement)40.”
From Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes (2025) · cites this paper as [40]
Section 3.1.3 mentions a review of AIGC in education but gives no learning-outcome comparisons. Searches for 17.2%, 15%, learning management systems, multimedia tools, and adaptive learning platforms found no passage supporting the attributed comparison; the cited paper focuses on visual art creation. Page numbers were not available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365 · see citing works
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332 · see citing works
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025 · see citing works
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L., Rao, A., & Agrawala, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00355 · Cited by 4,547 · see citing works
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P., & Nichol, A. (2021) · arXiv · doi:10.48550/arxiv.2105.05233 · Cited by 3,362 · see citing works
Semantic Image Synthesis With Spatially-Adaptive Normalization
Park, T., Liu, M.-Y., Wang, T.-C., & Zhu, J.-Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00244 · Cited by 2,974 · see citing works
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566 · see citing works
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.11487 · Cited by 2,528 · see citing works
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 2,523 · see citing works
Taming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01268 · Cited by 2,514 · see citing works
Hierarchical Text-Conditional Image Generation with CLIP Latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511 · cited 1× · see citing works
“From an artistic perspective, the first two categories characterize different art forms/genres with corresponding features.”
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02155 · Cited by 2,295 · see citing works
Instructpix2pix: Learning to follow image editing instructions
Brooks, T., Holynski, A., & Efros, A. A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01764 · Cited by 1,466 · see citing works
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., & Chen, M. (2021) · arXiv · doi:10.48550/arxiv.2112.10741 · Cited by 1,357 · see citing works
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Patashnik, O., Wu, Z., Shechtman, E., Cohen‐Or, D., & Lischinski, D. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00209 · Cited by 1,079 · see citing works
Mou, C., Wang, X., Xie, L., Wu, Y., Zhang, J., Qi, Z., & Shan, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28226 · Cited by 1,026 · see citing works
Proceedings of the 29th ACM International Conference on Multimedia
Yue, J., Li, H., Wei, P., Li, G., & Lin, L. (2021) · doi:10.1145/3474085 · Cited by 927 · see citing works
Neural Style Transfer: A Review
Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., & Song, M. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2921336 · Cited by 894 · see citing works
Imagic: Text-Based Real Image Editing with Diffusion Models
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., & Irani, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00582 · Cited by 813 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737 · see citing works
Multi-Concept Customization of Text-to-Image Diffusion
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., & Zhu, J.-Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00192 · Cited by 674 · see citing works
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., & Yoon, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01410 · Cited by 665 · see citing works
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., & Cohen-Or, D. (2022) · arXiv · doi:10.48550/arxiv.2208.01618 · Cited by 611 · see citing works
GLIGEN: Open-Set Grounded Text-to-Image Generation
Li, Y., Liu, H., Wu, Q., Mu, F., Yang, J., Gao, J., Li, C., & Lee, Y. J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02156 · Cited by 610 · see citing works
Understanding and creating art with AI: Review and outlook
Cetinic, E., & She, J. (2022) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3475799 · Cited by 579 · see citing works
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion ModelsPreprint
Zhao, S., Chen, D.-D., Chen, Y.-C., et al. (2023) · Neural Information Processing Systems · doi:10.48550/arxiv.2305.16322 · Cited by 507 · see citing works
Xia, W., Zhang, Y., Yang, Y., Xue, J., Zhou, B., & Yang, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3181070 · Cited by 496 · see citing works
Prompt-to-Prompt Image Editing with Cross Attention ControlPreprint
Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., & Cohen‐Or, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.01626 · Cited by 496 · see citing works
Art, Creativity, and the Potential of Artificial Intelligence
Mazzone, M., & Elgammal, A. (2019) · Arts · doi:10.3390/arts8010026 · Cited by 494 · see citing works
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Xu, M., Du, H., Niyato, D. T., Kang, J., Xiong, Z., Mao, S., Han, Z., Jamalipour, A., Kim, D. I., Shen, X., et al. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3353265 · Cited by 475 · see citing works
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023) · arXiv · doi:10.48550/arxiv.2303.04226 · Cited by 463 · see citing works
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Chefer, H., Alaluf, Y., Vinker, Y., Wolf, L., & Cohen-Or, D. (2023) · ACM Transactions on Graphics · doi:10.1145/3592116 · Cited by 461 · see citing works
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
Cao, M., Wang, X., Qi, Z., Shan, Y., Qie, X., & Zheng, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02062 · Cited by 443 · see citing works
Zero-shot Image-to-Image Translation
Parmar, G., Singh, K. K., Zhang, R., Li, Y., Lu, J., & Zhu, J.-Y. (2023) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3588432.3591513 · Cited by 383 · see citing works
Inversion-based Style Transfer with Diffusion Models
Zhang, Y., Huang, N., Tang, F., Huang, H., Ma, C., Dong, W., & Xu, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00978 · Cited by 374 · see citing works
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., & Komatsuzaki, A. (2021) · arXiv · doi:10.48550/arxiv.2111.02114 · Cited by 358 · see citing works
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.07068 · Cited by 353 · see citing works
Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions
Haque, A., Tancik, M., Efros, A. A., Holynski, A., & Kanazawa, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01808 · Cited by 334 · see citing works
Compositional Visual Generation with Composable Diffusion Models
Liu, N., Li, S., Du, Y., Torralba, A. B., & Tenenbaum, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19790-1_26 · Cited by 302 · see citing works
State of the "Art”: A Taxonomy of Artistic Stylization Techniques for Images and Video
Kyprianidis, J. E., Collomosse, J. P., Wang, T., et al. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.160 · Cited by 280 · see citing works
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert DenoisersPreprint
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Zhang, Q., Kreis, K., Aittala, M., Aila, T., Laine, S., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.01324 · Cited by 253 · see citing works
Retrieval-Augmented Generation for AI-Generated Content: A Survey
Zhao, P., Zhang, H., Yu, Q., Wang, Z., Geng, Y., Fu, F., Yang, L., Zhang, W., Jiang, J., & Cui, B. (2024) · arXiv · doi:10.48550/arxiv.2402.19473 · Cited by 240 · see citing works
Self-citationEmpowering education development through AIGC: A systematic literature review
Chen, X., Hu, Z., & Wang, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12549-7 · Cited by 222 · see citing works
Miller, A. I. (2019) · The MIT Press eBooks · doi:10.7551/mitpress/11585.001.0001 · Cited by 221 · see citing works
Autonomy, Authenticity, Authorship and Intention in Computer Generated Art
McCormack, J., Gifford, T., & Hutchings, P. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16667-0_3 · Cited by 218 · see citing works
What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education
Dehouche, N., & Dehouche, K. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16757 · Cited by 217 · see citing works
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., & Goldstein, T. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00586 · Cited by 206 · see citing works
Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works
Ko, H.-K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces · doi:10.1145/3581641.3584078 · Cited by 189 · see citing works
Art Therapy: A Complementary Treatment for Mental Disorders
Hu, J., Zhang, J., Hu, L., Yu, H., & Xu, J. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.686005 · Cited by 186 · see citing works
Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting
Wang, S., Saharia, C., Montgomery, C., Pont-Tuset, J., Noy, S., Pellegrini, S., Onoe, Y., Laszlo, S., Fleet, D. J., Soricut, R., et al. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01761 · Cited by 166 · see citing works
Muse: Text-To-Image Generation via Masked Generative Transformers
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K., Freeman, W. T., Rubinstein, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2301.00704 · Cited by 157 · see citing works
Zhang, K., Kolkin, N., Bi, S., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_41 · Cited by 156 · see citing works
Shahriar, S. (2022) · Displays · doi:10.1016/j.displa.2022.102237 · Cited by 147 · see citing works
MultiDiffusion: Fusing Diffusion Paths for Controlled Image GenerationPreprint
Bar-Tal, O., Yariv, L., Lipman, Y., & Dekel, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.08113 · Cited by 137 · see citing works
Art Therapy in the Digital World: An Integrative Review of Current Practice and Future Directions
Zubala, A., Kennell, N., & Hackett, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.600070 · Cited by 136 · see citing works
Castellano, G., & Vessio, G. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05893-z · Cited by 134 · see citing works
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
Zhang, Y., Dong, W., Tang, F., Huang, N., Huang, H., Ma, C., Lee, T., Deußen, O., & Xu, C. (2023) · ACM Transactions on Graphics · doi:10.1145/3618342 · Cited by 111 · see citing works
PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions
Chung, J. J. Y., & Adar, E. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606777 · Cited by 101 · see citing works
When Creators Meet the Metaverse: A Survey on Computational ArtsPreprint
Lee, L., Lin, Z., Hu, R., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13486 · Cited by 95 · see citing works
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models
Jain, A., Xie, A., & Abbeel, P. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00190 · Cited by 93 · see citing works
DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations
Qi, T., Fang, S., Wu, Y., Xie, H., Liu, J., Chen, L., He, Q., & Zhang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00830 · Cited by 91 · see citing works
An Implicit Parametric Morphable Dental Model
Zhang, C., Elgharib, M. A., Fox, G., et al. (2022) · ACM Transactions on Graphics · doi:10.1145/3550454.3555469 · Cited by 84 · see citing works
Cho, J. (2021) · Electronics · doi:10.3390/electronics10040470 · Cited by 80 · see citing works
Cultural Heritage Preservation of Traditional Indian Art through Virtual New-media
Kolay, S. (2016) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2016.06.030 · Cited by 77 · see citing works
Integrating art into science education: a survey of science teachers’ practices
Turkka, J., Haatainen, O., & Aksela, M. K. (2017) · International Journal of Science Education · doi:10.1080/09500693.2017.1333656 · Cited by 72 · see citing works
The Artist in the Machine: The World of AI-Powered Creativity
Miller, A. I. (2019) · Cited by 72 · see citing works
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Huang, N., Zhang, Y., Tang, F., Ma, C., Huang, H., Dong, W., & Xu, C. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3342645 · Cited by 63 · see citing works
ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank
Zhang, Z., Zhang, Q., Wei, X., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28570 · Cited by 63 · see citing works
Zero-shot spatial layout conditioning for text-to-image diffusion models
Couairon, G., Careil, M., Cord, M., Lathuilière, S., & Verbeek, J. J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00207 · Cited by 55 · see citing works
Transparent Image Layer Diffusion using Latent Transparency
Zhang, L., & Agrawala, M. (2024) · ACM Transactions on Graphics · doi:10.1145/3658150 · Cited by 55 · see citing works
Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes
Ju, X., Zeng, A., Wang, J., Xu, Q., & Zhang, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00067 · Cited by 54 · see citing works
ArtVerse: A Paradigm for Parallel Human–Machine Collaborative Painting Creation in Metaverses
Guo, C., Dou, Y., Bai, T., Dai, X., Wang, C., & Wen, Y. (2023) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2022.3230406 · Cited by 53 · see citing works
Zhang, J., Miao, Y., & Yu, J. (2021) · IEEE Access · doi:10.1109/access.2021.3083075 · Cited by 52 · see citing works
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion
Huang, N., Tang, F., Dong, W., & Xu, C. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548282 · Cited by 51 · see citing works
Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation
Bai, Z., Nakashima, Y., & García, N. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00537 · Cited by 48 · see citing works
dePolo, G. E., Walton, M. S., Keune, K., et al. (2021) · Heritage Science · doi:10.1186/s40494-021-00529-w · Cited by 46 · see citing works
Initial Images: Using Image Prompts to Improve Subject Representation in Multimodal AI Generated Art
Qiao, H., Liu, V., & Chilton, L. (2022) · Creativity and Cognition · doi:10.1145/3527927.3532792 · Cited by 46 · see citing works
Neural Canvas: Supporting Scenic Design Prototyping by Integrating 3D Sketching and Generative AI
Shen, Y., Shen, Y., Cheng, J., Jiang, C., Fan, M., & Wang, Z. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642096 · Cited by 46 · see citing works
Self-citationState of the Art in Artistic Editing of Appearance, Lighting and Material
Schmidt, T., Pellacini, F., Nowrouzezahrai, D., et al. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12721 · Cited by 44 · see citing works
A comprehensive survey on object detection in Visual Art: taxonomy and challenge
Bengamra, S., Mzoughi, O., Bigand, A., & Zagrouba, E. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15968-9 · Cited by 44 · see citing works
EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models
Yang, J., Feng, J., & Huang, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00608 · Cited by 41 · see citing works
Character-Preserving Coherent Story Visualization
Song, Y.-Z., Tam, Z. R., Chen, H.-J., Lu, H.-H., & Shuai, H.-H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58520-4_2 · Cited by 40 · see citing works
DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models
Ahn, N., Lee, J., Lee, C., Kim, K., Kim, D., Nam, S.-H., & Hong, K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27824 · Cited by 37 · see citing works
TypeDance: Creating Semantic Typographic Logos from Image through Personalized Generation
Xiao, S., Wang, L., Ma, X., & Zeng, W. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642185 · Cited by 37 · see citing works
End-To-End Time-Lapse Video Synthesis From a Single Outdoor Image
Nam, S., Ma, C., Chai, M., Brendel, W., Xu, N., & Kim, S. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00150 · Cited by 36 · see citing works
Boosting the performance of anomalous diffusion classifiers with the proper choice of features
Kowalek, P., Loch-Olszewska, H., Łaszczuk, Ł., et al. (2022) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ac6d2a · Cited by 36 · see citing works
Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review
Liu, L., Miteva, T., Delnevo, G., et al. (2023) · Sensors · doi:10.3390/s23052419 · Cited by 35 · see citing works
From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art
Maerten, A.-S., & Soydaner, D. (2026) · Art & Perception · doi:10.1163/22134913-bja10078 · Cited by 33 · see citing works
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis
Wu, Y., Nakashima, Y., & García, N. (2023) · International Conference on Multimedia Retrieval · doi:10.1145/3591106.3592262 · Cited by 32 · see citing works
Interactive Story Visualization with Multiple Characters
Gong, Y., Pang, Y., Cun, X., Xia, M., He, Y., Chen, H., Wang, L., Zhang, Y., Wang, X., Shan, Y., et al. (2023) · SIGGRAPH Asia Conference Papers · doi:10.1145/3610548.3618184 · Cited by 32 · see citing works
DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion
Tanveer, M., Wang, Y., Amiri, A. M., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00041 · Cited by 32 · see citing works
Zeng, X., Gao, Z., Ye, Y., & Zeng, W. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642165 · Cited by 30 · see citing works
Zhong, S., Huang, Z., Gao, S., Wen, W., Lin, L., Žitnik, M., & Zhou, P. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01258 · Cited by 30 · see citing works
Neural Painters: A learned differentiable constraint for generating brushstroke paintingsPreprint
Nakano, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08410 · Cited by 29 · see citing works
Nam, J., Kim, H., Lee, D., Jin, S., Kim, S., & Chang, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00774 · Cited by 26 · see citing works
Bidgoli, A., de Guevara, M. L., Hsiung, C., et al. (2020) · IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) · doi:10.1109/ro-man47096.2020.9223533 · Cited by 25 · see citing works
CCLAP: Controllable Chinese Landscape Painting Generation Via Latent Diffusion Model
Wang, Z., Zhang, J., Ji, Z., et al. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00362 · Cited by 21 · see citing works
Self-citationTracing versus freehand for evaluating computer-generated drawings
Wang, Z., Qiu, S., Feng, N., et al. (2021) · ACM Transactions on Graphics · doi:10.1145/3450626.3459819 · Cited by 20 · see citing works
Self-citationPainterly Image Harmonization using Diffusion Model
Lu, L., Li, J., Cao, J., Niu, L., & Zhang, L. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612451 · Cited by 20 · see citing works
Liang, C., Wu, X., Yang, H., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.04578 · Cited by 19 · see citing works
An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and OptimizationPreprint
Chen, M., Song, M., Fan, J., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.07771 · Cited by 19 · see citing works
Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage
Cioni, D., Berlincioni, L., Becattini, F., & Bimbo, A. D. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00186 · Cited by 17 · see citing works
Multi‐style Chinese art painting generation of flowers
Fu, F., Lv, J., Tang, C., & Li, M. (2020) · IET Image Processing · doi:10.1049/ipr2.12059 · Cited by 16 · see citing works
From Expanded Cinema to Extended Reality: How AI Can Expand and Extend Cinematic Experiences
Song, J., Wang, B., Wang, Z., & Yip, D. K.-M. (2023) · Proceedings of the 16th International Symposium on Visual Information Communication and Interaction · doi:10.1145/3615522.3615556 · Cited by 16 · see citing works
Self-citationTransformation vs Tradition: Artificial General Intelligence (AGI) for Arts and HumanitiesPreprint
Liu, Z., Li, Y., Cao, Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.19626 · Cited by 16 · see citing works
The ArtBench Dataset: Benchmarking Generative Models with ArtworksPreprint
Liao, P., Li, X., Liu, X., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.11404 · Cited by 15 · see citing works
Zhou, C., Zhong, F., & Öztireli, C. (2023) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3588432.3591532 · Cited by 14 · see citing works
Automatic Controllable Colorization via Imagination
Cong, X., Wu, Y., Chen, Q., & Lei, C. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00252 · Cited by 14 · see citing works
Self-citationAI-Generated Content (AIGC) for Various Data Modalities: A SurveyPreprint
Foo, L. G., Rahmani, H., & Liu, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.14177 · Cited by 13 · see citing works
A diffusion probabilistic model for traditional Chinese landscape painting super-resolution
Lyu, Q., Zhao, N., Yang, Y., Gong, Y., & Gao, J. (2024) · Heritage Science · doi:10.1186/s40494-023-01123-y · Cited by 13 · see citing works
CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields
Zhang, D., Fernandez-Labrador, C., & Schroers, C. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00022 · Cited by 13 · see citing works
Ordered Attention for Coherent Visual Storytelling
Braude, T., Schwartz, I., Schwing, A., et al. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548161 · Cited by 12 · see citing works
Editing Motion Graphics Video via Motion Vectorization and Transformation
Zhang, S., Ma, J., Wu, J., Ritchie, D., & Agrawala, M. (2023) · ACM Transactions on Graphics · doi:10.1145/3618316 · Cited by 12 · see citing works
Future Ink: The Collision of AI and Chinese Calligraphy
Lin, T., She, J. P. M., Wang, Y.-A., et al. (2024) · Journal on Computing and Cultural Heritage · doi:10.1145/3700882 · Cited by 12 · see citing works
MagicScroll: Enhancing Immersive Storytelling with Controllable Scroll Image Generation
Wang, B., Meng, H., Cao, R., Cai, Z., Li, L., Ma, Y., Chen, Q., & Wang, Z. (2025) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr59515.2025.00067 · Cited by 12 · see citing works
Self-citationAffective Analysis of Abstract Paintings Using Statistical Analysis and Art Theory
Sartori, A. (2014) · International Conference on Multimodal Interaction · doi:10.1145/2663204.2666289 · Cited by 11 · see citing works
Castellano, G., & Vessio, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68796-0_35 · Cited by 10 · see citing works
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large ModelPreprint
Bie, F., Yang, Y., Zhou, Z., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.00810 · Cited by 10 · see citing works
Intelligent-paint: a Chinese painting process generation method based on vision transformer
Wang, Z., Liu, F., Liu, F., et al. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01316-w · Cited by 10 · see citing works
Self-citationA Survey of Diffusion Based Image Generation Models: Issues and Their SolutionsPreprint
Zhang, T., Wang, Z., Huang, J., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.13142 · Cited by 9 · see citing works
Self-citationCreativeSeg: Semantic Segmentation of Creative Sketches
Zheng, Y., Pang, K., Das, A., Chang, D., Song, Y.-Z., & Ma, Z. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3374196 · Cited by 9 · see citing works
Generation of Chinese ink portraits by blending face photographs with Chinese ink paintings
Chiang, P.-Y., Lin, C.-V., & Tseng, C.-H. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.02.002 · Cited by 8 · see citing works
Naturality: A Natural Reflection of Chinese Calligraphy
Wang, B., Kang Zhang, A., & Wang, Z. (2023) · International Symposium on Visual Information Communication and Interaction (VINCI) · doi:10.1145/3615522.3615557 · Cited by 8 · see citing works
Self-citationInteractive3D: Create What You Want by Interactive 3D Generation
Dong, S., Ding, L., Huang, Z., Wang, Z., Xue, T., & Xu, D. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00478 · Cited by 8 · see citing works
Self-citationU-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation
Wu, Y., Liu, K., Mi, X., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00906 · Cited by 8 · see citing works
StyleAdapter: A Unified Stylized Image Generation ModelPreprint
Wang, Z., Wang, X., Xie, L., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.01770 · Cited by 7 · see citing works
Self-citationJin, X., Li, B., Xie, B., Zhang, W., Liu, J., Li, Z., Yang, T., & Zeng, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72995-9_16 · Cited by 7 · see citing works
RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth DiffusionPreprint
Shriram, J., Trevithick, A., Liu, L., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.07199 · Cited by 6 · see citing works
D’Amico, S., Venuti, V., Colica, E., et al. (2021) · ACTA IMEKO · doi:10.21014/acta_imeko.v10i1.824 · Cited by 5 · see citing works
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese PoemsPreprint
Li, D., Wang, S., Zou, J., Tian, C., Nieuwburg, E., Sun, F., & Kanoulas, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.11682 · Cited by 5 · see citing works
DiffStyler: Controllable Dual Diffusion for Text-Driven Image StylizationPreprint
Huang, N., Zhang, Y., Tang, F., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.10682 · Cited by 5 · see citing works
Zhang, L., Wong, T., & Liu, Y. (2022) · ACM Transactions on Graphics · doi:10.1145/3550454.3555439 · Cited by 5 · see citing works
Overview of Artificial Intelligence Painting Development and Some Related Model Application
Liu, M. (2023) · SHS Web of Conferences · doi:10.1051/shsconf/202316701004 · Cited by 5 · see citing works
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAsPreprint
Shah, V., Ruiz, N., Cole, F., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.13600 · Cited by 5 · see citing works
Portrait Diffusion: Training-free Face Stylization with Chain-of-PaintingPreprint
Liu, J., Huang, H., Jin, C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.02212 · Cited by 4 · see citing works
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMsPreprint
Yang, L., Yu, Z., Meng, C., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.11708 · Cited by 4 · see citing works
ARtVista: Gateway To Empower Anyone Into Artist
Hoang, T.-V., Nguyen, Q.-B., Ly, D.-N., Le, K.-D., Nguyen, T., Tran, M., & Le, T.-N. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650760 · Cited by 4 · see citing works
Ink Harmony: An AI- and VR-enhanced System for Calligraphy Education
Zhuo, Y., Wang, B., & Wang, Z. (2024) · doi:10.1145/3641521.3680540 · Cited by 4 · see citing works
Self-citationCreative Text-to-Image Generation: Suggestions for a Benchmark
Russo, I. (2022) · doi:10.18653/v1/2022.nlp4dh-1.18 · Cited by 4 · see citing works
Real-Time 3D Artistic Rendering System
Lee, T., Yan, S.-U., Chen, Y.-N., et al. (2005) · Lecture notes in computer science · doi:10.1007/11553939_65 · Cited by 3 · see citing works
Everyone Can Be Picasso? A Computational Framework into the Myth of Human versus AI PaintingPreprint
Ye, Y., Huang, R., Kang Zhang, A., & Zeng, W. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.07999 · Cited by 3 · see citing works
Simonstown: An AI-facilitated Interactive Story of Love, Life, and Pandemic
Wang, B., Zhu, P., Li, H., et al. (2023) · International Symposium on Visual Information Communication and Interaction (VINCI) · doi:10.1145/3615522.3615554 · Cited by 3 · see citing works
Self-citationEvaluating Text-to-Visual Generation with Image-to-Text GenerationPreprint
Lin, Z., Pathak, D., Li, B., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.01291 · Cited by 3 · see citing works
Ciallo: GPU-Accelerated Rendering of Vector Brush Strokes
Ciao, S., Guan, Z., Liu, Q., et al. (2024) · ACM SIGGRAPH Conference Papers (SIGGRAPH) · doi:10.1145/3641519.3657418 · Cited by 3 · see citing works
Replication in Visual Diffusion Models: A Survey and Outlook
Wang, W., Sun, Y., Yang, Z., et al. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3713990 · Cited by 3 · see citing works
Ai, H., & Lu, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.02343 · Cited by 2 · see citing works
Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion ModelsPreprint
Geng, D., Park, I., & Owens, A. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.17919 · Cited by 2 · see citing works
Brain-Conditional Multimodal Synthesis: A Survey and TaxonomyPreprint
Mai, W., Zhang, J., Fang, P., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.00430 · Cited by 2 · see citing works
EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion ModelsPreprint
Yang, J., Feng, J., & Huang, H. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.04608 · Cited by 2 · see citing works
Zeng, X., Gao, Z., Ye, Y., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.15559 · Cited by 2 · see citing works
Zhang, Z., Zhang, Q., Lin, H., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.11474 · Cited by 2 · see citing works
Dynamic Typography: Bringing Text to Life via Video Diffusion Prior
Liu, Z., Meng, Y., Ouyang, H., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01372 · Cited by 2 · see citing works
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback LearningPreprint
Chen, W., Zhang, J., Wu, J., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.15449 · Cited by 2 · see citing works
SonicDiffusion: Audio-Driven Image Generation and Editing with Pretrained Diffusion ModelsPreprint
Biner, B. C., Sofian, F. M., Karakaş, U. B., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.00878 · Cited by 2 · see citing works
Wired Perspectives: Multi-View Wire Art Embraces Generative AIPreprint
Qu, Z., Yang, L., Zhang, H., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.15421 · Cited by 1 · see citing works
Zhong, S., Huang, Z., Gao, S., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2312.02439 · Cited by 1 · see citing works
Jin, X., Li, B., Xie, B., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.02346 · Cited by 1 · see citing works
Nam, J., Kim, H., Lee, D., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.09812 · Cited by 1 · see citing works
Layout-to-Image Generation with Localized Descriptions using ControlNet with Cross-Attention ControlPreprint
Lukovnikov, D., & Fischer, A. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.13404 · Cited by 1 · see citing works
Transparent Image Layer Diffusion using Latent TransparencyPreprint
Zhang, L., & Agrawala, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.17113 · Cited by 1 · see citing works
U-VAP: User-specified Visual Appearance Personalization via Decoupled Self AugmentationPreprint
Wu, Y., Liu, K., Mi, X., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.20231 · Cited by 1 · see citing works
Strictly-ID-Preserved and Controllable Accessory Advertising Image GenerationPreprint
Xue, Y., Chen, B., Geng, Y., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.04828 · Cited by 1 · see citing works
Automatic Controllable Colorization via ImaginationPreprint
Cong, X., Wu, Y., Chen, Q., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.05661 · Cited by 1 · see citing works
Self-citationMultiBooth: Towards Generating All Your Concepts in an Image from TextPreprint
Zhu, C., Li, K., Ma, Y., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.14239 · Cited by 1 · see citing works
CoARF: Controllable 3D Artistic Style Transfer for Radiance FieldsPreprint
Zhang, D., Fernandez-Labrador, C., & Schroers, C. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.14967 · Cited by 1 · see citing works
GLoD: Composing Global Contexts and Local Details in Image GenerationPreprint
Yamada, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.15447 · Cited by 1 · see citing works
Interactive3D: Create What You Want by Interactive 3D GenerationPreprint
Dong, S., Ding, L., Huang, Z., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.16510 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 29
- Nilin Abrahamsen and Jiahao Yao. 2023. Inventing art styles with no artistic training data. arXiv preprint arXiv:2305.12015 (2023). https://api.semanticscholar.org/CorpusID:258833473 · arXiv
- Qingyan Bai, Yinghao Xu, Zifan Shi, Hao Ouyang, Qiuyu Wang, Ceyuan Yang, Xuan Wang, Gordon Wetzstein, Yujun Shen, and Qifeng Chen. 2024. Real-time 3D-aware portrait editing from a single image. arXiv preprint arXiv:2402.14000 (2024). · arXiv
- Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712 (2023). · arXiv
- Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. 2023. PixArt-α : Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426 (2023). · arXiv
- Xing Cui, Zekun Li, Peipei Li, Huaibo Huang, Xuannan Liu, and Zhaofeng He. 2024. INSTASTYLE: Inversion noise of a stylized image is secretly a style adviser. In Proceedings of the European Conference on Computer Vision. 455-472.
- Nicola Dall'Asen, Willi Menapace, Elia Peruzzo, Enver Sangineto, Yiming Wang, and Elisa Ricci. 2023. Collaborative neural painting. arXiv preprint arXiv:2312.01800 (2023). · arXiv
- Denis Dutton. 2009. The Art Instinct: Beauty, Pleasure, & Human Evolution. Oxford University Press, USA.
- Xiao-Nan Fang, Bin Liu, and Ariel Shamir. 2018. Automatic thread painting generation. arXiv preprint arXiv:1802.04706 (2018). · arXiv
- Kunyu Feng, Yue Ma, Bingyuan Wang, Chenyang Qi, Haozhe Chen, Qifeng Chen, and Zeyu Wang. 2024. Dit4Edit: Diffusion transformer for image editing. arXiv preprint arXiv:2411.03286 (2024). · arXiv
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems 27 (2014), 1-9.
- Yingqing He, Shaoshu Yang, Haoxin Chen, Xiaodong Cun, Menghan Xia, Yong Zhang, Xintao Wang, Ran He, Qifeng Chen, and Ying Shan. 2023. ScaleCrafter: Tuning-free higher-resolution visual generation with diffusion models. In Proceedings of the 12th International Conference on Learning Representations.
- Irina Higgins, Loic Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mo- hamed, and Alexander Lerchner. 2017. Beta-VAE: Learning basic visual concepts with a constrained variational framework. In Proceedings of the the 5th International Conference on Learning Representations (ICLR'17): Poster.
- Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022). · arXiv
- Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013). · arXiv
- Chenghao Li, Chaoning Zhang, Atish Waghwase, Lik-Hang Lee, Francois Rameau, Yang Yang, Sung-Ho Bae, and Choong Seon Hong. 2023. Generative AI meets 3D: A survey on text-to-3D in AIGC era. arXiv preprint arXiv:2305.06131 (2023). · arXiv
- Hao Li, Zhongyue Guan, and Zeyu Wang. 2024. An inverse procedural modeling pipeline for stylized brush stroke rendering. In Proceedings of the 45th Annual Conference of the European Association for Computer Graphics (EURO- GRAPHICS'24).
- Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, and Qiang Liu. 2023. InstaFlow: One step is enough for high- quality diffusion-based text-to-image generation. In Proceedings of the 12th International Conference on Learning Representations.
- Jon McCormack and Mark D'Inverno. 2014. On the future of computers and creativity. In Proceedings of the AISB 2014 Symposium on Computational Creativity.
- Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervi- sion. In Proceedings of the International Conference on Machine Learning. 8748-8763.
- Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In Proceedings of the International Conference on Machine Learning. 8821-8831.
- Kihyuk Sohn, Lu Jiang, Jarred Barber, Kimin Lee, Nataniel Ruiz, Dilip Krishnan, Huiwen Chang, Yuanzhen Li, Irfan Essa, Michael Rubinstein, et al. 2024. StyleDrop: Text-to-image synthesis in any style. Advances in Neural Information Processing Systems 36 (2024), 66860-66889.
- Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020). · arXiv
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 1-11.
- Zeyu Wang. 2022. Enhancing the Creative Process in Digital Prototyping. Ph. D. Dissertation. Yale University.
- Xianchao Wu. 2022. Creative painting with latent diffusion models. In Proceedings of the 2nd Workshop on When Creative AI Meets Conversational AI. 59-80.
- Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T. Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, et al. 2023. WonderJourney: Going from anywhere to everywhere. arXiv preprint arXiv:2312.03884 (2023). · arXiv
- Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon. 2023. Text-to-image diffusion model in generative AI: A survey. arXiv preprint arXiv:2303.07909 (2023). · arXiv
- Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019. Self-attention generative adversarial networks. In Proceedings of the International Conference on Machine Learning. 7354-7363.
- Yinhan Zhang, Yue Ma, Bingyuan Wang, Qifeng Chen, and Zeyu Wang. 2025. MagicColor: Multi-instance sketch colorization. arXiv preprint arXiv:2503.16948 (2025). · arXiv
Cited by
Sorted by citations
Yan, Z., & Qianjun, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-10941-y · Cited by 21 · see citing works
- Authors
- Wang, B., Chen, Q., & Wang, Z.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Venue
- ACM Computing Surveys
- Published
- 2025-05-07
- Volume
- 57
- Issue
- 10
- Pages
- 1-37
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4409182016
- Cited by
- 40
- References
- 175
- Citations by year
- 2026: 22 · 2025: 18
- FWCI
- 14.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Computing Surveys (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Guangzhou Basic Research (2024A04J4229)
- Guangzhou Education Bureau (2024312075)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Keywords
- diffusion models, generative AI, visual art creation, AI-generated art, creative process, artistic perception, computational creativity, image generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
