# EditGarment: An Instruction-Based Garment Editing Dataset Constructed with Automated MLLM Synthesis and Semantic-Aware Evaluation

arXiv: https://arxiv.org/abs/2508.03497 · HTML: https://mlsanity.com/paper/2508.03497/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://mlsanity.com/paper/2303.11897/) (2023)
- [Emu Edit: Precise Image Editing via Recognition and Generation Tasks](https://mlsanity.com/paper/2311.10089/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [An Edit Friendly DDPM Noise Space: Inversion and Manipulations](https://mlsanity.com/paper/2304.06140/) (2023)
- [Diffusion Model-Based Image Editing: A Survey](https://mlsanity.com/paper/2402.17525/) (2024)
- [HIVE: Harnessing Human Feedback for Instructional Visual Editing](https://mlsanity.com/paper/2303.09618/) (2023)
- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On](https://mlsanity.com/paper/2305.13501/) (2023)
- [Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models](https://mlsanity.com/paper/2411.07232/) (2024)
- [EditAR: Unified Conditional Generation with Autoregressive Models](https://mlsanity.com/paper/2501.04699/) (2025)
- [iEdit: Localised Text-guided Image Editing with Weak Supervision](https://mlsanity.com/paper/2305.05947/) (2023)
- [ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design](https://mlsanity.com/paper/2208.05621/) (2022)
- [Learning to Follow Object-Centric Image Editing Instructions Faithfully](https://mlsanity.com/paper/2310.19145/) (2023)
- [GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections](https://mlsanity.com/paper/2408.12352/) (2024)
- [Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2403.14828/) (2024)
- [HiGarment: Cross-modal Harmony Based Diffusion Model for Flat Sketch to Realistic Garment Image](https://mlsanity.com/paper/2505.23186/) (2025)

## Built on by (most cited first, 1 of 1)

- [Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off](https://mlsanity.com/paper/2603.22607/) (2026)

## Abstract (arXiv)

Instruction-based garment editing enables precise image modifications via natural language, with broad applications in fashion design and customization. Unlike general editing tasks, it requires understanding garment-specific semantics and attribute dependencies. However, progress is limited by the scarcity of high-quality instruction-image pairs, as manual annotation is costly and hard to scale. While MLLMs have shown promise in automated data synthesis, their application to garment editing is constrained by imprecise instruction modeling and a lack of fashion-specific supervisory signals. To address these challenges, we present an automated pipeline for constructing a garment editing dataset. We first define six editing instruction categories aligned with real-world fashion workflows to guide the generation of balanced and diverse instruction-image triplets. Second, we introduce Fashion Edit Score, a semantic-aware evaluation metric that captures semantic dependencies between garment attributes and provides reliable supervision during construction. Using this pipeline, we construct a total of 52,257 candidate triplets and retain 20,596 high-quality triplets to build EditGarment, the first instruction-based dataset tailored to standalone garment editing. The project page is https://yindq99.github.io/EditGarment-project/.