Image based virtual try-on network from unpaired data
This paper presents a new image-based virtual try-on approach (Outfit-VITON) that helps visualize how a composition of clothing items selected from various reference images form a cohesive outfit on a person in a query image. Our algorithm has two distinctive properties. First, it is inexpensive, as it simply requires a large set of single (non-corresponding) images (both real and catalog) of people wearing various garments without explicit 3D information. The training phase requires only single images, eliminating the need for manually creating image pairs, where one image shows a person wearing a particular garment and the other shows the same catalog garment alone. Secondly, it can synthesize images of multiple garments composed into a single, coherent outfit; and it enables control of the type of garments rendered in the final outfit. Once trained, our approach can then synthesize a cohesive outfit from multiple images of clothed human models, while fitting the outfit to the body shape and pose of the query person. An online optimization step takes care of fine details such as intricate textures and logos. Quantitative and qualitative evaluations on an image dataset containing large shape and style variations demonstrate superior accuracy compared to existing state-of-the-art methods, especially when dealing with highly detailed garments.