PFNet: Attribute-aware personalized fashion editing with explainable fashion compatibility analysis

Attribute-aware editing provides a feasible way for users to participate in fashion design. In a sense, an explainable model of fashion compatibility can assist users to perceive the fashionability of their design. However, previous fashion editing researches are opaque in compatibility and rely on labels to manipulate the editing of specific coarse-grained attributes. Consequently, we propose a novel attribute-aware personalized fashion editing network with explainable fashion compatibility modeling, named PFNet, which can simultaneously decouple entangled attributes to make them editable and generate an attribute-wise compatibility explanation for fashion design. In PFNet, we propose an unsupervised garment attribute decoupling network, which independently encodes attributes by hierarchical style control and minimizing mutual correlation. Besides, we develop an attribute compatibility-aware attention network to deeply explore compatible interactions of attributes and visualize their internal decisions. The empirical experiments and user study on the FashionVC and Polyvore datasets reveal that the decoupling accuracy of PFNet for multiple clothing attributes is increased by an average of 22% compared to the state-of-the-art method, and provides more popular compatibility insights with an accuracy rate of 75.5%.