  Recently, the progress of learning-by-synthesis has proposed a training model
for synthetic images, which can effectively reduce the cost of human and
material resources. However, due to the different distribution of synthetic
images compared to real images, the desired performance cannot be achieved.
Real images consist of multiple forms of light orientation, while synthetic
images consist of a uniform light orientation. These features are considered to
be characteristic of outdoor and indoor scenes, respectively. To solve this
problem, the previous method learned a model to improve the realism of the
synthetic image. Different from the previous methods, this paper try to purify
real image by extracting discriminative and robust features to convert outdoor
real images to indoor synthetic images. In this paper, we first introduce the
segmentation masks to construct RGB-mask pairs as inputs, then we design a
attention-guided style transfer network to learn style features separately from
the attention and bkgd(background) region , learn content features from full
and attention region. Moreover, we propose a novel region-level task-guided
loss to restrain the features learnt from style and content. Experiments were
performed using mixed studies (qualitative and quantitative) methods to
demonstrate the possibility of purifying real images in complex directions. We
evaluate the proposed method on three public datasets, including LPW, COCO and
MPIIGaze. Extensive experimental results show that the proposed method is
effective and achieves the state-of-the-art results.
