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.

翻译：生成式人工智能（AI）的进步催生了多种能够生成令人惊叹的视觉设计输出的AI模型。然而，在将AI模型应用于设计流程时，相较于单纯的视觉工艺，优先输出符合设计师需求的结果变得更为关键。此外，设计师常常将不同设计的部件进行精细组合以创造新颖设计。能够在部件级别生成符合设计师意图的设计方案，对于辅助设计实践至关重要。为此，我们提出了BOgen系统，该系统通过贝叶斯优化与变分自编码器，使设计师能够主动生成和探索部件级设计，从而提升其整体用户体验。我们通过包含30名设计师的研究评估了BOgen的性能。结果表明，与基准方法相比，BOgen满足了设计师对部件推荐和设计探索空间引导的需求。该系统能够辅助设计师进行导航与设计演进，提供有价值的设计建议，并促进主动的设计探索与创作。