DualFit: A Two-Stage Virtual Try-On via Warping and Synthesis

TL;DR

DualFit is a two-stage virtual try-on system that warps garments with a learned flow and synthesizes realistic images, preserving fine details like logos and text for improved visual fidelity and brand integrity.

Contribution

It introduces a hybrid pipeline combining warping and synthesis with preserved-region guidance, enhancing detail preservation in virtual try-on applications.

Findings

Achieves seamless try-on with high-detail preservation.

Balances reconstruction accuracy and perceptual realism.

Outperforms existing methods in detail retention.

Abstract

Virtual Try-On technology has garnered significant attention for its potential to transform the online fashion retail experience by allowing users to visualize how garments would look on them without physical trials. While recent advances in diffusion-based warping-free methods have improved perceptual quality, they often fail to preserve fine-grained garment details such as logos and printed text elements that are critical for brand integrity and customer trust. In this work, we propose DualFit, a hybrid VTON pipeline that addresses this limitation by two-stage approach. In the first stage, DualFit warps the target garment to align with the person image using a learned flow field, ensuring high-fidelity preservation. In the second stage, a fidelity-preserving try-on module synthesizes the final output by blending the warped garment with preserved human regions. Particularly, to guide this process, we introduce a preserved-region input and an inpainting mask, enabling the model to retain key areas and regenerate only where necessary, particularly around garment seams. Extensive qualitative results show that DualFit achieves visually seamless try-on results while faithfully maintaining high-frequency garment details, striking an effective balance between reconstruction accuracy and perceptual realism.