Sketch2Topo: Using Hand-Drawn Inputs for Diffusion-Based Topology Optimization
This is an incremental improvement for engineers and designers in structural optimization, focusing on usability and aesthetics.
The paper tackles the problem of limited customizability and high cognitive load in topology optimization by developing Sketch2Topo, a tool that uses hand-drawn sketches and diffusion models to enable user customization and improve usability, though no concrete performance numbers are provided.
Topology optimization (TO) is employed in engineering to optimize structural performance while maximizing material efficiency. However, traditional TO methods incur significant computational and time costs. Although research has leveraged generative AI to predict TO outcomes and validated feasibility and accuracy, existing approaches still suffer from limited customizability and impose a high cognitive load on users. Furthermore, balancing structural performance with aesthetic attributes remains a persistent challenge. We developed Sketch2Topo, which augments a diffusion-based TO model with image-to-image generation and image editing capabilities. With Sketch2Topo, users can use sketching to customize geometries and specify physical constraints. The tool also supports mask input, enabling users to perform TO on selected regions only, thereby supporting higher levels of customization. We summarize the workflow and details of the tool and conduct a brief quantitative evaluation. Finally, we explore application scenarios and discuss how hand-drawn input improves usability while balancing functionality and aesthetics.