InstructMesh: Selective Refinement of Generative 3D Models for Fabrication

2026-08-28

AI Summary

为解决生成式3D模型几何精度不足的问题，InstructMesh通过区域选择和针对性操作提供交互式修复工具，用户可通过自然语言或滑块控制进行编辑。

Abstract

Recent advances in generative AI allow users to create 3D models from text or images. However, these models prioritize visual plausibility over geometric accuracy, often generating results with flaws that compromise their intended use post-fabrication. We present InstructMesh, an interactive post-generation refinement tool that enables selective repair of generative 3D models through region selection and targeted operations, such as opening or sealing voids, or adjusting local thickness. Users can invoke edit operations via natural language prompts or slider controls. By operating directly on the intermediate latent representation, InstructMesh allows users to apply robust geometric corrections without requiring expert modeling skills. To inform our design, we first analyze common fabrication-related failure modes in outputs from state-of-the-art generative tools. We then conduct two user studies, demonstrating that novices can identify and perform fabrication-relevant repairs on generative outputs using InstructMesh, and revealing user preference for hybrid interfaces that combine slider controls with natural language input.

Problem

• Generative 3D Models
• Geometric Accuracy
• Fabrication

Innovation

• Interactive Post-Generation Refinement
• Selective Repair
• Natural Language Prompts
• Latent Representation
• Hybrid Interfaces