Virtual Try-on is the ability to realistically superimpose clothing onto a target person. Due to its importance to the multi-billion dollar e-commerce industry, the problem has received significant attention in recent years. To date, most virtual try-on methods have been supervised approaches, namely using annotated data, such as clothes parsing semantic segmentation masks and paired images. These approaches incur a very high cost in annotation. Even existing weakly-supervised virtual try-on methods still use annotated data or pre-trained networks as auxiliary information and the costs of the annotation are still significantly high. Plus, the strategy using pre-trained networks is not appropriate in the practical scenarios due to latency. In this paper we propose Unsupervised VIRtual Try-on using disentangled representation (UVIRT). After UVIRT extracts a clothes and a person feature from a person image and a clothes image respectively, it exchanges a clothes and a person feature. Finally, UVIRT achieve virtual try-on. This is all achieved in an unsupervised manner so UVIRT has the advantage that it does not require any annotated data, pre-trained networks nor even category labels. In the experiments, we qualitatively and quantitatively compare between supervised methods and our UVIRT method on the MPV dataset (which has paired images) and on a Consumer-to-Consumer (C2C) marketplace dataset (which has unpaired images). As a result, UVIRT outperform the supervised method on the C2C marketplace dataset, and achieve comparable results on the MPV dataset, which has paired images in comparison with the conventional supervised method.
Keywords: GAN; disentanglement; image-to-image translation; unsupervised learning; virtual try-on.