Abstract Virtual try‐on is a highly active line of research, with increasing demand. Its task is replacing 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 generalisation and imposing heavy computation. In this paper, we present a novel zero‐shot training‐free method for embedding a clothing garment by reference. Our approach employs the prior of a diffusion model with no additional training, fully leveraging its native generalisation 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 to encourage garment preservation while eliminating leakage of reference background and unwanted influence. Through a user study and a qualitative and quantitative comparison to state‐of‐the‐art approaches, we demonstrate superior image quality and garment preservation for unseen clothing pieces.
Orzech et al. (Mon,) studied this question.