Cascaded Cross-Domain Fusion of Virtual Try-On

Xinrong Hu, Junyu Zhang, Tao Peng, Mingfu Xiong, Feng Yu, Li Li, Min Li

Abstract: Image-based virtual try-on, aiming to fit new in-shop clothes into a person image, has gained extensive attention in the fields of computer vision and image process community. However, the existing methods are difficult to generate photo-realistic try-on images when large-scale deformations or large occlusions occur. To address this issue, we propose a novel two stage visual try-on network. Specifically, in the first stage, we used a shape matching model to learn the geometric transformation of in-shop clothes. For the second stage, an U-net with cascaded attention mechanism is presented to learn the composition mask which adjust the clothes and rendered persons. The adjusted clothes and the rendered person are combined by the composition mask to get the final try-on result. Experimental results have shown that our method can generate photo-realistic images with no occlusion.