From Jewellery Design Sketch to Realistic Product Imagery Using Gemini Artificial Intelligence

Traditionally, transforming jewellery design concepts into final products has relied on manual rendering and physical prototyping—a process that is often time-intensive, costly, and dependent on technical expertise. Recent advancements in generative artificial intelligence (AI) present new opportunities to streamline and enhance this workflow. This study investigates the application of Gemini Artificial Intelligence in converting jewellery design sketches into realistic product imagery, effectively bridging the gap between conceptualisation and market-ready visualisation. A structured methodology was applied, including sketch digitisation, prompt-driven AI interpretation, iterative refinement, and validation by jewellery professionals. The resulting outputs were assessed based on realism, design fidelity, material representation, and commercial applicability. Findings indicate that Gemini AI can generate high-quality jewellery visuals closely resembling professional renders while significantly reducing development time. The study underscores the potential of AI-assisted visualisation in supporting designers, small-scale entrepreneurs, and educational institutions within the jewellery industry, contributing to the growing body of knowledge on AI integration in creative sectors.

Jewellery design, Gemini AI, Sketch-to-image, Product visualization, Artificial intelligence

1. Chen, Y., Liu, H., & Zhao, Q. (2023). AI-assisted product visualisation for rapid prototyping in industrial design. Journal of Design Automation, 18(2), 112–128. [Google Scholar] [Crossref]

2. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851. [Google Scholar] [Crossref]

3. Isola, P., Zhu, J., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1125–1134. [Google Scholar] [Crossref]

4. Kumar, R., Patel, S., & Mehta, D. (2021). Digital transformation in jewellery manufacturing through CAD and visualization technologies. International Journal of Creative Engineering, 7(3), 45–59. [Google Scholar] [Crossref]

5. Li, X., & Wang, Y. (2021). AI-based architectural visualization from conceptual sketches. Automation in Construction, 125, 103614. [Google Scholar] [Crossref]

6. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125. [Google Scholar] [Crossref]

7. Singh, P., & Jain, R. (2020). Computer-aided design applications in modern jewellery production. Journal of Manufacturing Technology, 29(4), 567–579. [Google Scholar] [Crossref]

8. Zhang, L., Wu, J., & Chen, K. (2022). AI-driven fashion illustration using sketch-to-image synthesis. Design Studies, 78, 101056. [Google Scholar] [Crossref]

• The Impact of Ownership Structure on Dividend Payout Policy of Listed Plantation Companies in Sri Lanka
• Urban Sustainability in North-East India: A Study through the lens of NER-SDG index
• Performance Assessment of Predictive Forecasting Techniques for Enhancing Hospital Supply Chain Efficiency in Healthcare Logistics
• The Fractured Self in Julian Barnes' Postmodern Fiction: Identity Crisis and Deflation in Metroland and the Sense of an Ending
• Impact of Flood on the Employment, Labour Productivity and Migration of Agricultural Labour in North Bihar