让普通用户轻松修复生成3D模型的制造缺陷。
InstructMesh: Selective Refinement of Generative 3D Models for Fabrication
- 通过自然语言或滑块选择区域，精准修复模型缺陷。
- 支持开孔、封口、调厚度等操作，提升模型可制造性。
- 适合无建模经验者使用，融合语言与滑块的交互设计。
近期生成式AI使用户能通过文本或图像创建3D模型，但这些模型常因侧重视觉逼真而忽略几何精度，导致生成结果存在缺陷，影响后续制造。我们提出InstructMesh，一种交互式后处理修复工具，支持通过区域选择和针对性操作（如开孔、封口或调整局部厚度）修复生成3D模型。用户可通过自然语言提示或滑块控制执行编辑。该工具直接作用于中间潜在表示，实现稳健的几何修正，无需专业建模技能。为指导设计，我们首先分析了主流生成工具输出中常见的制造相关缺陷。随后开展两项用户研究，表明新手能借助InstructMesh识别并完成制造相关的修复任务，且更偏好结合滑块与自然语言输入的混合界面。
原文摘要 · Abstract (English)
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.
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