Digital Technology Empowers Innovation in Calligraphy and Painting Creation: Research on Simulation of Brush and Ink Styles and Personalized Creation Based on Generative AI
Main Article Content
Abstract
The digital preservation and innovative expression of calligraphy and painting styles require accurate modeling of brushstroke morphology, ink diffusion characteristics, and personalized artistic features. To address the limitations of existing digital creation systems in style simulation accuracy and personalized generation capability, this study proposes a brush-and-ink style simulation framework based on generative artificial intelligence. The proposed method integrates deep generative adversarial networks and diffusion models to perform high-dimensional feature encoding, brushstroke parameter modeling, ink-flow representation, and semantic-guided style generation. A human–computer interaction module is further developed to support personalized artistic creation through controllable semantic and stylistic inputs. Experimental evaluation on 5,000 calligraphy and painting samples demonstrates that the proposed framework achieves a brushstroke restoration accuracy of 93.8% and significantly improves style similarity compared with conventional convolutional approaches. The results confirm the effectiveness of generative AI for digital artistic reproduction and personalized creation. The proposed framework also provides methodological references for image generation, visual information processing, and intelligent pattern synthesis applications.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Y. Chen, “Research on the Construction of Personalized Creative Teaching Model for Primary School Art under the Background of Artificial Intelligence,” China New Communications, vol. 27, no. 15, pp. 239-241, 2025, doi: 10.3969/j.issn.1673-4866.2025.15.082.
P. Wu, “A Study on the Application of Personalized Teaching in Primary School Chinese Composition Instruction,” Teaching Management and Educational Research, vol. 8, no. 12, pp. 28-30, 2023, doi: 10.3969/j.issn.2096-224X.2023.12.009.
Z. Jiang and P. Liao, “Research on the Personalized Development and Expression of Contemporary Ceramic Flower-and-Bird Painting,” Jingdezhen Ceramics, vol. 51, no. 6, pp. 72-74, 2023, doi: 10.3969/j.issn.1006-9545.2023.06.023.
S. Chen, “A Study on the Application of Paper in Painting Creation,” China Paper Industry, vol. 44, no. 23, pp. 97-99, 2023, doi: 10.3969/j.issn.1007-9211.2023.23.039.
P. Pataranutaporn, V. Danry, J. Leong, et al., “AI-generated characters for supporting personalized learning and wellbeing,” Nature Machine Intelligence, vol. 3, no. 12, pp. 1013-1022, 2021, doi: 10.1038/s42256-021-00417-9.
E. Cetinic and J. She, “Understanding and creating art with AI: Review and outlook,” ACM transactions on multimedia computing, communications, and applications (TOMM), vol. 18, no. 2, pp. 1-22, 2022, doi: 10.1145/3475799.
M. Masood, M. Nawaz, M. Malik K, et al., “Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward: Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward,” Applied intelligence, vol. 53, no. 4, pp. 3974-4026, 2023, doi: 10.1007/s10489-022-03766-z.
J. Oppenlaender, “A taxonomy of prompt modifiers for text-to-image generation,” Behaviour & Information Technology, vol. 43, no. 15, pp. 3763-3776, 2024, doi: 10.1080/0144929X.2023.2286532.
J. Oppenlaender, R. Linder, and J. Silvennoinen, “Prompting AI art: An investigation into the creative skill of prompt engineering,” International journal of human–computer interaction, vol. 41, no. 16, pp. 10207-10229, 2025, doi: 10.1080/10447318.2024.2431761.
Y. Wang, X. Chen, X. Ma, et al., “Lavie: High-quality video generation with cascaded latent diffusion models,” International Journal of Computer Vision, vol. 133, no. 5, pp. 3059-3078, 2025, doi: 10.1007/s11263-024-02295-1.