UVTON: UV Mapping to Consider the 3D Structure of a Human in Image Based Virtual Try On Network
Image based virtual try on is an area of research that is attracting attention as the demand for online apparel shopping continues to increase. The methods proposed thus far have focused on how to generate a dress up image while preserving the clothing details. However, the posture of the model in the image is limited to an upright position, and other positions frequently do not work well. In this study, based on a kind of generative adversarial network (GAN) that utilizes UV mapping to consider the 3D structure of the human body, we propose a novel virtual try on method called a UV Try On Network (UVTON) We use a DensePose to estimate a point corresponding to the 3D surface of a human model for each pixel point of a 2D image and incorporate the estimated information into our model. It is thus possible to change the clothes of users holding various postures. Our proposed method uses UV mapping and two other modules. One module generates parts to be used in the mapping, and the other refines the image and produces a more realistic image. Based on both qualitative and quantitative comparison with existing methods, we experimentally demonstrated that our method achieved better results with various postures.
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)