Masked Extended Attention for Zero-Shot Virtual Try-On In The Wild

TL;DR

This paper introduces a zero-shot, training-free virtual try-on method using extended attention and diffusion models, achieving superior image quality without extensive training or supervision.

Contribution

It proposes a novel zero-shot inpainting approach leveraging diffusion models and extended attention, eliminating the need for training data and improving generalization.

Findings

Outperforms state-of-the-art methods in image quality and garment preservation.

Effectively handles unseen clothing and human figures.

Reduces computational complexity compared to supervised approaches.

Abstract

Virtual Try-On (VTON) is a highly active line of research, with increasing demand. It aims to replace a piece of garment in an image with one from another, while preserving person and garment characteristics as well as image fidelity. Current literature takes a supervised approach for the task, impairing generalization and imposing heavy computation. In this paper, we present a novel zero-shot training-free method for inpainting a clothing garment by reference. Our approach employs the prior of a diffusion model with no additional training, fully leveraging its native generalization capabilities. The method employs extended attention to transfer image information from reference to target images, overcoming two significant challenges. We first initially warp the reference garment over the target human using deep features, alleviating "texture sticking". We then leverage the extended attention mechanism with careful masking, eliminating leakage of reference background and unwanted influence. Through a user study, qualitative, and quantitative comparison to state-of-the-art approaches, we demonstrate superior image quality and garment preservation compared unseen clothing pieces or human figures.