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

2026-04-23

AI Summary

Traditional 2.5D topology optimization methods are limited in design diversity, customizability, and the integration of structural performance with aesthetic intent. This work proposes TopoStyle—the first 2.5D topology optimization framework that synergistically combines generative AI with interactive iterative design. Built upon a 2D diffusion model, TopoStyle introduces a region-masking mechanism and supports dual-modal interaction through hand-drawn sketches and point-and-click selection within 3D modeling software. This approach enables users to flexibly embed aesthetic preferences while preserving structural integrity, thereby facilitating efficient, personalized design. Extensive case studies demonstrate TopoStyle’s effectiveness in enhancing design efficiency, expanding expressive freedom, and achieving a balanced trade-off between performance and aesthetics, alongside a systematic evaluation of the suitability of different interaction modalities.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Computer Vision: Diffusion Models for Vision

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• topology optimization
• iterative design
• generative AI
• customizability
• aesthetics

Innovation

• generative AI
• topology optimization
• iterative design
• diffusion model
• interactive customization