Toward Detail-Oriented Image-Based Virtual Try-On with Arbitrary Poses

Image-based virtual try-on with arbitrary poses has attracted many attentions recently. The purpose of this study is to synthesize a reference person image wearing a target clothes with a target pose. However, it is still a challenge for the existing methods to preserve the clothing details and person identity while generating fine-grained tryon images. To resolve the issues, we present a new detail-oriented virtual try-on network with arbitrary poses (DO-VTON). Specifically, our DO-VTON consists of three major modules: First, a semantic prediction module adopts a two-stage strategy to gradually predict a semantic map of the reference person. Second, a spatial alignment module warps the target clothes and non-target details to align with the target pose. Third, a try-on synthesis module generates final try-on images. Moreover, to generate high-quality images, we introduce a new multi-scale dilated convolution U-Net to enlarge the receptive field and capture context information. Extensive experiments on two famous benchmark datasets demonstrate our system achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.