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Image-based virtual try-on provides the capacity to transfer a clothing itemonto a photo of a given person, which is usually accomplished by warping theitem to a given human pose and adjusting the warped item to the person.However, the results of real-world synthetic images (e.g., selfies) from theprevious method is not realistic because of the limitations which result in theneck being misrepresented and significant changes to the style of the garment.To address these challenges, we propose a novel method to solve this uniqueissue, called VITON-CROP. VITON-CROP synthesizes images more robustly whenintegrated with random crop augmentation compared to the existingstate-of-the-art virtual try-on models. In the experiments, we demonstrate thatVITON-CROP is superior to VITON-HD both qualitatively and quantitatively.

Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)