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

2026-03-19

AI Summary

This work proposes a diffusion model–based topology optimization framework that addresses the high computational cost and limited interactivity of traditional methods, which often struggle to balance structural performance with aesthetic considerations. For the first time, the approach incorporates hand-drawn sketches as user input and employs mask-guided image-to-image generation to enable localized structural refinement. By allowing users to specify custom geometries and physical constraints, the method significantly enhances design freedom and interaction intuitiveness. Experimental results demonstrate that the proposed framework not only preserves mechanical performance but also enriches aesthetic expressiveness, with quantitative evaluations confirming its effectiveness and computational efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• topology optimization
• customizability
• cognitive load
• aesthetic attributes
• computational cost

Innovation

• diffusion-based topology optimization
• hand-drawn sketch input
• image-to-image generation
• mask-guided editing
• customizable structural design