This study discusses the critical issues of Virtual Try-On in contemporary
e-commerce and the prospective metaverse, emphasizing the challenges of
preserving intricate texture details and distinctive features of the target
person and the clothes in various scenarios, such as clothing texture and
identity characteristics like tattoos or accessories. In addition to the
fidelity of the synthesized images, the efficiency of the synthesis process
presents a significant hurdle. Various existing approaches are explored,
highlighting the limitations and unresolved aspects, e.g., identity information
omission, uncontrollable artifacts, and low synthesis speed. It then proposes a
novel diffusion-based solution that addresses garment texture preservation and
user identity retention during virtual try-on. The proposed network comprises
two primary modules - a warping module aligning clothing with individual
features and a try-on module refining the attire and generating missing parts
integrated with a mask-aware post-processing technique ensuring the integrity
of the individual's identity. It demonstrates impressive results, surpassing
the state-of-the-art in speed by nearly 20 times during inference, with
superior fidelity in qualitative assessments. Quantitative evaluations confirm
comparable performance with the recent SOTA method on the VITON-HD and
Dresscode datasets.

本研究讨论了当代电子商务和前景元宇宙中虚拟试穿的关键问题，强调在各种场景中保留目标人物和衣物的复杂纹理细节和独特特征所面临的挑战，并探索了现有方法的局限性和未解决的问题，随后提出了一种基于扩散的新颖解决方案，该解决方案在虚拟试穿过程中解决了服装纹理保留和用户身份保留的问题。该网络显示出令人印象深刻的结果，在推理过程中的速度超过最先进技术近 20 倍，并在定性评估中具有更高的保真度。在 VITON-HD 和 Dresscode 数据集上的定量评估证实了与最近的 SOTA 方法相当的性能。