Learning intrinsic decomposition of complex-textured fashion images

In this paper, we present a deep learning approach for shading decomposition from single garment images with complex textured albedos. This work propose a multi-stream generator to infer albedo, shading, normal and lighting in a unified network and train it in a novel local-global adversarial learning framework. To support the training, we carefully build a large dataset with synthetic garment photos under various lighting conditions and textured albedos. Both quantitative and qualitative evaluations, on synthetic and real images, demonstrate the superiority of our method against the state-of-the-art.