End to End Learning of Geometric Deformations of Feature Maps for Virtual Try On
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.
ArXiv