Method and system for recoloring a product

Abstract

State of the art techniques have challenges for recoloring a product, which includes non-realistic images, incorrect color mapping, structural distortion, color spilling into background, and in handling multi-color, multi-apparel and multi-product scenario images. Embodiments of the present disclosure provide a method and system for recoloring a product using a dual attention (DA) U-Net based on a generative adversarial network (GAN) framework to generate a recolored product with a target color from an input image. The disclosed DAU-Net enables recoloring (i) a single-color in a single-product scenario, (ii) a plurality of colors in a single-product scenario, and (iii) multi-product scenario with a human model. The DAU net uses (i) a product components aware feature (PCAF) extraction to generate feature representations comprising information of the target color with finer details, and (b) a critical feature selection (CFS) mechanism applied on the feature representation, to generate enhanced feature representations.