  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/.
