Coarse-to-Fine Clothing Image Generation with Progressively Constructed Conditional GAN

Abstract

Clothing image generation is a task of generating clothing product images from input fashion images of people dressed. Results of existing GAN based methods often contain visual artifact with the global consistency issue. To solve this issue, we split the difficult single image generation process into relatively easy multiple stages for image generation process. We thus propose a coarse-to-fine strategy for the image-conditional image generation model, with a multi-stage network training method, called rough-to-detail training. We incrementally add a decoder block for each stage that progressively configures an intermediate target image, to make the generator network appropriate for rough-to-detail training. With this coarse-to-fine process, our model can generate from small size images with rough structures to large size images with details. To validate our model, we perform various quantitative comparisons and human perception study on the LookBook dataset. Compared to other conditional GAN methods, our model can create visually pleasing 256 × 256 clothing images, while keeping the global structure and containing details of target images.