Search for: All records

Fashion attribute editing aims to manipulate fashion im- ages based on a user-specified attribute, while preserving the details of the original image as intact as possible. Re- cent works in this domain have mainly focused on direct manipulation of the raw RGB pixels, which only allows to perform edits involving relatively small shape changes (e.g., sleeves). The goal of our Virtual Personal Tailoring Network (VPTNet) is to extend the editing capabilities to much larger shape changes of fashion items, such as cloth length. To achieve this goal, we decouple the fashion at- tribute editing task into two conditional stages: shape-then- appearance editing. To this aim, we propose a shape editing network that employs a semantic parsing of the fashion im- age as an interface for manipulation. Compared to operat- ing on the raw RGB image, our parsing map editing enables performing more complex shape editing operations. Sec- ond, we introduce an appearance completion network that takes the previous stage results and completes the shape dif- ference regions to produce the final RGB image. Qualitative and quantitative experiments on the DeepFashion-Synthesis dataset confirm that VPTNet outperforms state-of-the-art methods for both small and large shape attribute editing.