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.

该研究提出了一种新颖的零训练、自由输入方法，通过参考图像实现衣物修复，利用扩展关注机制从参考图像向目标图像传递图像信息，通过用户研究和与现有方法的定量和定性比较，证明了在处理未见过的服装或人物时优于其他方法的图像质量和服装保护能力。