# TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices

arXiv: https://arxiv.org/abs/2410.18085 · HTML: https://mlsanity.com/paper/2410.18085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [MIMIC-IT: Multi-Modal In-Context Instruction Tuning](https://mlsanity.com/paper/2306.05425/) (2023)
- [DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models](https://mlsanity.com/paper/2210.10606/) (2022)
- [Re-Identification Supervised Texture Generation](https://mlsanity.com/paper/1904.03385/) (2019)
- [DefectTwin: When LLM Meets Digital Twin for Railway Defect Inspection](https://mlsanity.com/paper/2409.06725/) (2024)
- [uTalk: Bridging the Gap Between Humans and AI](https://mlsanity.com/paper/2310.02739/) (2023)

## Abstract (arXiv)

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.