Image-Based Virtual Try-on Network with Structural Coherence

Abstract

Virtual try-on system could demonstrate the visual effect of wearing certain clothes, which is in a great demand for the online clothing customers. Due to the neglect of the original person structure, the previous try-on schemes usually encounter with the problems like the loss of body parts, the missing of body details and the deviation of clothing style. In this paper, we propose a novel image-based virtual try-on network, which could maintain the structural consistency between the generated image and the original image by human parsing. Our network consists of three components. Given the original person and the clothing images, the target human parsing maps are generated. Then, the parsing maps are matched with the target clothes to generate the warped clothes. Finally, according to the parsing results, the parts to be replaced the original images are intercepted, and more original information is retained as the input of the network to generate the final results. Experiments on an existing benchmark demonstrate our method maintains the consistency of structure and achieves the state-of-the-art performance.