Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of the garment, while warping the garment to accommodate a significant body pose and shape change across the subjects. Previous methods either focus on garment detail preservation without effective pose and shape variation, or allow try-on with the desired shape and pose but lack garment details. In this paper, we propose a diffusion-based architecture that unifies two UNets (referred to as Parallel-UNet), which allows us to preserve garment details and warp the garment for significant pose and body change in a single network. The key ideas behind Parallel-UNet include: 1) garment is warped implicitly via a cross attention mechanism, 2) garment warp and person blend happen as part of a unified process as opposed to a sequence of two separate tasks. Experimental results indicate that TryOnDiffusion achieves state-of-the-art performance both qualitatively and quantitatively.
@article{arxiv.2306.08276,
title = {TryOnDiffusion: A Tale of Two UNets},
author = {Luyang Zhu and Dawei Yang and Tyler Zhu and Fitsum Reda and William Chan and Chitwan Saharia and Mohammad Norouzi and Ira Kemelmacher-Shlizerman},
journal= {arXiv preprint arXiv:2306.08276},
year = {2023}
}