We present a novel and effective approach for generating new clothing on a wearer through generative adversarial learning. Given an input image of a person and a sentence describing a different outfit, our model "redresses" the person as desired, while at the same time keeping the wearer and her/his pose unchanged. Generating new outfits with precise regions conforming to a language description while retaining wearer's body structure is a new challenging task. Existing generative adversarial networks are not ideal in ensuring global coherence of structure given both the input photograph and language description as conditions. We address this challenge by decomposing the complex generative process into two conditional stages. In the first stage, we generate a plausible semantic segmentation map that obeys the wearer's pose as a latent spatial arrangement. An effective spatial constraint is formulated to guide the generation of this semantic segmentation map. In the second stage, a generative model with a newly proposed compositional mapping layer is used to render the final image with precise regions and textures conditioned on this map. We extended the DeepFashion dataset [8] by collecting sentence descriptions for 79K images. We demonstrate the effectiveness of our approach through both quantitative and qualitative evaluations. A user study is also conducted. The codes and the data are available at http://mmlab.ie.cuhk. edu.hk/projects/FashionGAN/.

本文提出了一种通过生成对抗学习在穿着者身上生成新的服装的新颖有效方法，该方法通过语言描述并在保持穿着者及其姿势不变的同时恰当地更换服装，并通过将复杂的生成过程分解为两个有条件的阶段来解决现有生成对抗网络中的结构相容性问题，该文章通过收集79K张图片的句子描述和定量、定性测试展示了该方法的有效性。

成为自己的普拉达：具有结构连贯性的时尚合成