  The 2D virtual try-on task has recently attracted a lot of interest from the
research community, for its direct potential applications in online shopping as
well as for its inherent and non-addressed scientific challenges. This task
requires to fit an in-shop cloth image on the image of a person. It is highly
challenging because it requires to warp the cloth on the target person while
preserving its patterns and characteristics, and to compose the item with the
person in a realistic manner. Current state-of-the-art models generate images
with visible artifacts, due either to a pixel-level composition step or to the
geometric transformation. In this paper, we propose WUTON: a Warping U-net for
a Virtual Try-On system. It is a siamese U-net generator whose skip connections
are geometrically transformed by a convolutional geometric matcher. The whole
architecture is trained end-to-end with a multi-task loss including an
adversarial one. This enables our network to generate and use realistic spatial
transformations of the cloth to synthesize images of high visual quality. The
proposed architecture can be trained end-to-end and allows us to advance
towards a detail-preserving and photo-realistic 2D virtual try-on system. Our
method outperforms the current state-of-the-art with visual results as well as
with the Learned Perceptual Image Similarity (LPIPS) metric.
