用手机生成铁路部件缺陷纹理，让维修人员直观看到损伤效果。
TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices
- 基于大模型的移动端工具，可交互式生成真实缺陷纹理。
- 生成速度比传统方法快，移动端也能流畅运行。
- 适合铁路维护、工业质检人员快速模拟缺陷场景。
纹理图像生成已广泛应用于游戏和娱乐领域，但针对工业应用（如铁路部件缺陷纹理生成）的上下文相关真实感纹理生成仍属空白。本文提出TextureMeDefect，一个基于大模型的轻量级移动端AI推理工具，支持用户通过智能手机或平板相机拍摄的铁路部件图像，实时生成细粒度缺陷纹理。我们在iOS和Android平台进行了多维度评估，包括生成纹理的相关性、耗时与成本，并在三种使用场景下分析了软件可用性评分（SUS）。结果表明，该工具在生成有意义纹理方面优于传统图像生成方法，且运行效率更高，验证了在消费级设备上实现AI驱动移动应用的可行性。
原文摘要 · Abstract (English)
Texture image generation has been studied for various applications, including gaming and entertainment. However, context-specific realistic texture generation for industrial applications, such as generating defect textures on railway components, remains unexplored. A mobile-friendly, LLM-based tool that generates fine-grained defect characteristics offers a solution to the challenge of understanding the impact of defects from actual occurrences. We introduce TextureMeDefect, an innovative tool leveraging an LLM-based AI-Inferencing engine. The tool allows users to create realistic defect textures interactively on images of railway components taken with smartphones or tablets. We conducted a multifaceted evaluation to assess the relevance of the generated texture, time, and cost in using this tool on iOS and Android platforms. We also analyzed the software usability score (SUS) across three scenarios. TextureMeDefect outperformed traditional image generation tools by generating meaningful textures faster, showcasing the potential of AI-driven mobile applications on consumer-grade devices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。