Structure-Aware and Frequency-Guided Diffusion Framework for Multimodal Fashion Image Editing

Abstract

Fashion image editing aims to modify target garment attributes under textual and reference guidance while preserving non-target contents. However, existing methods often suffer from inaccurate garment localization, insufficient preservation of high-frequency textures, and unnatural transitions near edited boundaries. To address these issues, we propose a structure-aware and frequency-guided framework for multimodal fashion image editing. Specifically, we design a Structure-Guided Textual Mask Network to predict geometry-aware editing regions by leveraging refined textual structural cues and human-centric priors, where a structural prior reweighting mechanism is introduced to improve localization accuracy. We further develop an adaptive frequency-domain texture enhancement module to inject high-frequency fabric details from a reference image during late denoising, and employ a boundary-band soft fusion strategy to ensure smooth visual transitions. In addition, we construct a new dataset, DFEdit, for fine-grained multimodal fashion image editing. Experimental results show that the proposed method achieves competitive performance in terms of editing fidelity, texture consistency, and visual quality, showing its effectiveness for intelligent fashion image editing applications.