Sketch2Topo: Using Hand-Drawn Inputs for Diffusion-Based Topology Optimization

TL;DR

Sketch2Topo introduces a diffusion-based topology optimization tool that allows users to customize structures through sketching and mask inputs, enhancing usability and aesthetic control while reducing computational costs.

Contribution

The paper presents Sketch2Topo, a novel diffusion-based topology optimization framework integrating image editing and sketching for customizable, user-friendly structural design.

Findings

Supports sketch-based geometry customization

Enables region-specific topology optimization via masks

Balances structural performance with aesthetic preferences

Abstract

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.