Abstract
Advancements in generative artificial intelligence have introduced state-of-the-art models capable of producing impressive visual shape outputs. However, when it comes to supporting decisions during the three-dimensional shape creation process, prioritizing outputs that align with designers’ needs over mere visual craftsmanship becomes crucial. Furthermore, designers often intricately combine three-dimensional parts of various shapes to create novel designs. The ability to generate designs that align with the designers’ intentions at the part-level is pivotal for assisting designers. Hence, we introduced BOgen, a novel system that empowers designers to proactively generate and synthesize part-level three-dimensional shapes and enhances their overall user experience by reflecting designer intentions through Bayesian optimization. We assessed BOgen’s performance using a study involving 30 designers. The results revealed that, compared to the baseline, BOgen fulfilled the designer requirements for three-dimensional shape part recommendations and shape exploration space guidance. BOgen assists designers in navigation and development, offering design suggestions and fostering proactive design exploration and creation during early-stage design ideation.
Data availability
The data underlying this article will be shared on reasonable request to the corresponding author.
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Funding
This work was supported by the Technology Innovation Program (RS-2025-02317326, Development of AI-Driven Design Generation Technology Based on Designer Intent) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP: Ministry of Science, ICT and Future Planning) (RS-2023-00208542).
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S.W.L. led the conceptualization and methodology design, developed and prepared the original draft of the manuscript. J.C. contributed to writing, reviewing, and editing the manuscript. K.H.H. supervised the project and participated in writing, reviewing, and editing. All authors reviewed and approved the final version of the manuscript.
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Lee, S.W., Choi, J. & Hyun, K.H. Part-level 3D shape generation driven by user intention inference with preferential Bayesian optimization. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38916-7
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DOI: https://doi.org/10.1038/s41598-026-38916-7
