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We propose a flexible person generation framework called Dressing in Order(DiOr), which supports 2D pose transfer, virtual try-on, and several fashionediting tasks. The key to DiOr is a novel recurrent generation pipeline tosequentially put garments on a person, so that trying on the same garments indifferent orders will result in different looks. Our system can producedressing effects not achievable by existing work, including differentinteractions of garments (e.g., wearing a top tucked into the bottom or overit), as well as layering of multiple garments of the same type (e.g., jacketover shirt over t-shirt). DiOr explicitly encodes the shape and texture of eachgarment, enabling these elements to be edited separately. Joint training onpose transfer and inpainting helps with detail preservation and coherence ofgenerated garments. Extensive evaluations show that DiOr outperforms otherrecent methods like ADGAN in terms of output quality, and handles a wide rangeof editing functions for which there is no direct supervision.

Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing