Virtually Trying on New Clothing with Arbitrary Poses

Thanks to the recent advance in the multimedia techniques, increasing research attention has been paid to the virtual try-on task, especially with the 2D image modeling. The traditional try-on task aims to align the target clothing item naturally to the given person's body and hence present a try-on look of the person. However, in practice, people may also be interested in their try-on looks with different poses. Therefore, in this work, we introduce a new try-on setting, which enables the changes of both the clothing item and the person's pose. Towards this end, we propose a pose-guided virtual try-on scheme based on the generative adversarial networks (GANs) with a bi-stage strategy. In particular, in the first stage, we propose a shape enhanced clothing deformation model for deforming the clothing item, where the user body shape is incorporated as the intermediate guidance. For the second stage, we present an attentive bidirectional GAN, which jointly models the attentive clothing-person alignment and bidirectional generation consistency. For evaluation, we create a large-scale dataset, FashionTryOn, comprising $28,714$ triplets with each consisting of a clothing item image and two model images in different poses. Extensive experiments on FashionTryOn validate the superiority of our model over the state-of-the-art methods.