TailorGAN: Making User-Defined Fashion Designs

Lele Chen, Justin Tian, Guo Li, Cheng-Haw Wu, Erh-Kan King, Kuan-Ting Chen, Shao-Hang Hsieh, Chenliang Xu; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 3241-3250

Attribute editing has become an important and emerging topic of computer vision. In this paper, we consider a task: given a reference garment image A and another image B with target attribute (collar/sleeve), generate a photo-realistic image which combines the texture from reference A and the new attribute from reference B. The highly convoluted attributes and the lack of paired data are the main challenges to the task. To overcome those limitations, we propose a novel self-supervised model to synthesize garment images with disentangled attributes (e.g., collar and sleeves) without paired data. Our method consists of reconstruction learning step and adversarial learning step. The model learns texture and location information through reconstruction learning. And the model capability is generalized to achieve single-attribute manipulation by adversarial learning. Meanwhile, we compose a new dataset, named GarmentSet, with annotation of landmarks of collar and sleeves on clean garment images. Thoughtful experiments on this dataset and real-world samples demonstrate that our method can synthesize significantly better results than the state-of-the-art methods in both quantitative and qualitative comparisons. The code is available at: https://github.com/gli-27/TailorGAN.