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

TL;DR

This paper introduces EditGarment, a large-scale dataset for instruction-based garment editing, created through automated synthesis guided by fashion-specific semantics and evaluation, to advance fashion image editing research.

Contribution

It presents a novel automated pipeline for constructing a high-quality garment editing dataset with semantic-aware supervision and diverse instructions, addressing data scarcity in fashion editing tasks.

Findings

Constructed 20,596 high-quality instruction-image triplets.

Defined six real-world fashion instruction categories.

Introduced Fashion Edit Score for semantic-aware evaluation.

Abstract

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/.