MACHINE LEARNING MODELS FOR STYLE TRANSFER IN SCULPTURE
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6637Keywords:
Machine Learning, Style Transfer, Sculpture, Cyclegan, Diffusion Models, Neural Style TransferAbstract [English]
The application of machine learning to the field of sculpture has revitalised the boundaries of artistic invention and online production. The paper is about the high-order models of style transfer in sculpture which is the process of transferring visual characteristics of art in one type of sculpture to another not compromising on the structural integrity. Three models were employed including CycleGAN, Neural Style Transfer (NST) and Diffusion-based Generative Models. CycleGAN therefore facilitated unpaired data translation and hence was able to translate the material textures and sculptural patterns with an accuracy of style of 89.4. Neural Style Transfer had fine-grained aesthetic embedding at a texture fidelity of 85.7 percent on 3D surface renderings. Meanwhile, Diffusion Models were discovered to be more encouraging in spatial consistency and volumetric detailing with structural consistency of 92.1, superior to the traditional CNN-based techniques. The data were the digital scans of the classical and modern sculptures, the data were processed with the assistance of 3D voxel and mesh-based images. Experimentation has shown that a hybridization of these models is more expressive artistically and more efficient in calculations in the migration of sculptural style. The findings show that the latest AI-driven applications can not only imitate but also produce sculptural beauty and bridge the void between digital art and solid production with the assistance of 3D printing and CNC production. The piece is somehow related to the field of computational art in that it provides a possible paradigm of machine learning-like transfer to sculpture.
References
Agnihotri, K., Chilbule, P., Prashant, S., Jain, P., and Khobragade, P. (2023). Generating Image Description Using Machine Learning Algorithms. In 2023 11th International Conference on Emerging Trends in Engineering and Technology – Signal and Information Processing (ICETET–SIP) (1–6). IEEE. https://doi.org/10.1109/ICETET-SIP58143.2023.10151472 DOI: https://doi.org/10.1109/ICETET-SIP58143.2023.10151472
Ahmed, M., Kim, M. B., and Choi, K. S. (2025). PrecisionGAN: Enhanced Image-To-Image Translation for Preserving Structural Integrity in Skeletonized Images. International Journal on Document Analysis and Recognition (IJDAR), 28(2), 239–253. https://doi.org/10.1007/s10032-024-00505-7 DOI: https://doi.org/10.1007/s10032-024-00505-7
Alcantara-Rodriguez, M., Van Andel, T., and Françozo, M. (2024). Nature Portrayed in Images in Dutch Brazil: Tracing the Sources of the Plant Woodcuts in the Historia Naturalis Brasiliae (1648). PLOS ONE, 19(7), Article e0276242. https://doi.org/10.1371/journal.pone.0276242 DOI: https://doi.org/10.1371/journal.pone.0276242
Caciora, T., Jubran, A., Ilies, D. C., Hodor, N., Blaga, L., Ilies, A., … Herman, G. V. (2023). Digitization of the Built Cultural Heritage: An Integrated Methodology for Preservation and Accessibilization of an art Nouveau Museum. Remote Sensing, 15(24), Article 5763. https://doi.org/10.3390/rs15245763 DOI: https://doi.org/10.3390/rs15245763
Fang, X., Li, L., Gao, Y., Liu, N., and Cheng, L. (2024). Expressing the Spatial Concepts of Interior Spaces in Residential Buildings of Huizhou, China: Narrative Methods of Wood-Carving Imagery. Buildings, 14(5), Article 1414. https://doi.org/10.3390/buildings14051414 DOI: https://doi.org/10.3390/buildings14051414
Guo, D. H., Chen, H. X., Wu, R. L., and Wang, Y. G. (2023). AIGC Challenges and Opportunities Related to Public Safety: A Case Study of ChatGPT. Journal of Safety Science and Resilience, 4, 329–339. https://doi.org/10.1016/j.jnlssr.2023.08.001 DOI: https://doi.org/10.1016/j.jnlssr.2023.08.001
Jiang, H. (2025). Technology and Application of Digital Art Style Transfer Based on Deep Learning. In 2025 2nd International Conference on Informatics Education and Computer Technology Applications (IECA) (pp. 74–78). IEEE. https://doi.org/10.1109/IECA66054.2025.00021 DOI: https://doi.org/10.1109/IECA66054.2025.00021
Leong, W. Y. (2025). Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity. Engineering Proceedings, 92, Article 45. https://doi.org/10.3390/engproc2025092045 DOI: https://doi.org/10.3390/engproc2025092045
Martinez, O., and Sharma, D. (2025). Graph-Based Weighted KNN for Enhanced Classification Accuracy in Machine Learning. International Journal of Electrical Engineering and Computer Science (IJEECS), 14(1), 26–30.
Oksanen, A., Cvetkovic, A., Akin, N., Latikka, R., Bergdahl, J., Chen, Y., and Savela, N. (2023). Artificial Intelligence in Fine Arts: A Systematic Review of Empirical Research. Computers in Human Behavior: Artificial Humans, 1, Article 100004. https://doi.org/10.1016/j.chbah.2023.100004 DOI: https://doi.org/10.1016/j.chbah.2023.100004
Shi, Q., and Hou, J. (2025). Deep Learning-Based Style Transfer Research for Traditional Woodcut Paper Horse art Images. Journal of Computational Methods in Sciences and Engineering, 25(5), 4679–4697. https://doi.org/10.1177/14727978251337945 DOI: https://doi.org/10.1177/14727978251337945
Zhang, S., Qi, Y., and Wu, J. (2025). Applying Deep Learning for Style Transfer in Digital Art: Enhancing Creative Expression through Neural Networks. Scientific Reports, 15, Article 11744. https://doi.org/10.1038/s41598-025-95819-9 DOI: https://doi.org/10.1038/s41598-025-95819-9
Zhao, L., and Kim, J. (2024). The Impact of Traditional Chinese Paper-Cutting in Digital Protection for Intangible Cultural Heritage Under Virtual Reality Technology. Heliyon, 10(18), Article e38073. https://doi.org/10.1016/j.heliyon.2024.e38073 DOI: https://doi.org/10.1016/j.heliyon.2024.e38073
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Copyright (c) 2025 Dr. Pragati Pandit, Dr. Swarna Swetha Kolaventi, Rajesh Kulkarni, Vivek Saraswat, Gagan Tiwari, Sourav Rampal
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