  This paper proposes a flexible person generation framework called Dressing in
Order (DiOr), which supports 2D pose transfer, virtual try-on, and several
fashion editing tasks. Key to DiOr is a novel recurrent generation pipeline to
sequentially put garments on a person, so that trying on the same garments in
different orders will result in different looks. Our system can produce
dressing effects not achievable by existing work, including different
interactions of garments (e.g., wearing a top tucked into the bottom or over
it), as well as layering of multiple garments of the same type (e.g., jacket
over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each
garment, enabling these elements to be edited separately. Joint training on
pose transfer and inpainting helps with detail preservation and coherence of
generated garments. Extensive evaluations show that DiOr outperforms other
recent methods like ADGAN in terms of output quality, and handles a wide range
of editing functions for which there is no direct supervision.
