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.

本研究提出了一种基于扩散的架构，统一了两个并行 UNet，旨在在保留服装细节的同时，通过扭曲服装并令其适应不同的身体姿态和形状变化，生成着装效果图。实验结果表明，该方法在质量和多个评估指标上均取得了最先进的表现。