While significant progress has been made in garment transfer, one of the most
applicable directions of human-centric image generation, existing works
overlook the in-the-wild imagery, presenting severe garment-person misalignment
as well as noticeable degradation in fine texture details. This paper,
therefore, attends to virtual try-on in real-world scenes and brings essential
improvements in authenticity and naturalness especially for loose garment
(e.g., skirts, formal dresses), challenging poses (e.g., cross arms, bent
legs), and cluttered backgrounds. Specifically, we find that the pixel flow
excels at handling loose garments whereas the vertex flow is preferred for hard
poses, and by combining their advantages we propose a novel generative network
called wFlow that can effectively push up garment transfer to in-the-wild
context. Moreover, former approaches require paired images for training.
Instead, we cut down the laboriousness by working on a newly constructed
large-scale video dataset named Dance50k with self-supervised cross-frame
training and an online cycle optimization. The proposed Dance50k can boost
real-world virtual dressing by covering a wide variety of garments under
dancing poses. Extensive experiments demonstrate the superiority of our wFlow
in generating realistic garment transfer results for in-the-wild images without
resorting to expensive paired datasets.

本研究基于新构建的大规模视频数据集 Dance50k，提出了 wFlow 网络，通过像素传输和顶点传输进行虚拟试穿，优化了松散服装和具有挑战性姿势的效果。实验证明，该方法在不依赖昂贵的成对数据集的情况下，能够在野外图像中生成逼真的服装转换结果。