Style-Instructed Mask-Free Virtual Try On

TL;DR

This paper introduces a mask-free virtual try-on framework that uses attention guidance and instruction prompts to improve garment fitting accuracy and user interaction without relying on predefined masks.

Contribution

The proposed method eliminates the need for masks in virtual try-on, integrating attention guidance and prompts to enhance flexibility and performance.

Findings

Outperforms existing methods in accuracy and robustness

Produces more user-friendly and flexible try-on results

Works effectively across multiple datasets

Abstract

Virtual Try-On is a promising research area with broad applications in e-commerce and everyday life, enabling users to visualize garments on themselves or others before purchase. Most existing methods depend on predefined or user-specified masks to guide garment placement, but their performance is highly sensitive to mask quality, often causing misalignment or artifacts, and introduces redundant steps for users. To overcome these limitations, we propose a mask-free virtual try-on framework that requires only minimal modifications to the underlying architecture while remaining compatible with common diffusion-based pipelines. To address the increased ambiguity in the absence of masks, we integrate an attention-based guidance mechanism that explicitly directs the model to focus on the target garment region and improves correspondence between the garment and the person. Additionally, we incorporate instruction prompts, allowing users to flexibly control garment categories and wearing styles, addressing the underutilization of prompts in prior work and improving interaction flexibility. Both qualitative and quantitative evaluations across multiple datasets demonstrate that our approach consistently outperforms existing methods, producing more accurate, robust, and user-friendly try-on results.