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arXiv · 2604.21315

TopoStyle: Supporting Iterative Design with Generative AI for 2.5D Topology Optimization

Shuyue Feng·Cedric Caremel·Yoshihiro Kawahara
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Abstract

Topology optimization(TO) is widely used in engineering because of its ability to save material and optimize structural performance. Although prior work has explored 2D human-centered design tool for TO, the results are often limited in variety and offer weak customizability. Meanwhile, due to the high computational and time costs of TO, researchers have attempted to address these issues using generative AI; however, such methods often provide limited interactivity. In addition, topology optimization in many cases needs to balance structural performance and aesthetic qualities through iterative design, a perspective that has rarely been emphasized in traditional TO. We present TopoStyle, an iterative design tool for 2.5D topology optimization using a 2D diffusion model. We explore two interaction methods. The first exports 3D parts to a graphical interface for hand-drawn interaction. The second enables direct interaction within 3D modeling software using points. Our tool also supports the use of masks to apply topology optimization to specific regions, allowing users to address customized design needs. We compare and evaluate both performance and interaction methods, and investigate how TopoStyle can balance performance and aesthetics while improving design efficiency through customization and iterative design. Finally, we demonstrate the application scenarios of TopoStyle through several design cases.

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Shuyue Feng, Cedric Caremel, Yoshihiro Kawahara. 2026-04-23. TopoStyle: Supporting Iterative Design with Generative AI for 2.5D Topology Optimization. https://arxiv.org/abs/2604.21315

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