  Advancements in generative artificial intelligence (AI) have introduced
various AI models capable of producing impressive visual design outputs.
However, when it comes to AI models in the design process, prioritizing outputs
that align with designers' needs over mere visual craftsmanship becomes even
more crucial. Furthermore, designers often intricately combine parts of various
designs 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, which empowers designers to proactively
generate and explore part-level designs through Bayesian optimization and
variational autoencoders, thereby enhancing their overall user experience. 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 part recommendations and design exploration space guidance.
BOgen assists designers in navigation and development, offering valuable design
suggestions and fosters proactive design exploration and creation.
