LGVTON: A Landmark Guided Approach to Virtual Try-On

We address the problem of image based virtual try-on (VTON), where the goal is to synthesize an image of a person wearing the cloth of a model. An essential requirement for generating a perceptually convincing VTON result is preserving the characteristics of the cloth and the person. Keeping this in mind we propose \textit{LGVTON}, a novel self-supervised landmark guided approach to image based virtual try-on. The incorporation of self-supervision tackles the problem of lack of paired training data in model to person VTON scenario. LGVTON uses two types of landmarks to warp the model cloth according to the shape and pose of the person, one, human landmarks, the locations of anatomical keypoints of human, two, fashion landmarks, the structural keypoints of cloth. We introduce an unique way of using landmarks for warping which is more efficient and effective compared to existing warping based methods in current problem scenario. In addition to that, to make the method robust in cases of noisy landmark estimates that causes inaccurate warping, we propose a mask generator module that attempts to predict the true segmentation mask of the model cloth on the person, which in turn guides our image synthesizer module in tackling warping issues. Experimental results show the effectiveness of our method in comparison to the state-of-the-art VTON methods.