  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 a reconstruction learning step and an adversarial learning
step. The model learns texture and location information through reconstruction
learning. And, the model's capability is generalized to achieve
single-attribute manipulation by adversarial learning. Meanwhile, we compose a
new dataset, named GarmentSet, with annotation of landmarks of collars and
sleeves on clean garment images. Extensive experiments on this dataset and
real-world samples demonstrate that our method can synthesize much better
results than the state-of-the-art methods in both quantitative and qualitative
comparisons.
