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Recently, the progress of learning-by-synthesis has proposed a training modelfor synthetic images, which can effectively reduce the cost of human andmaterial resources. However, due to the different distribution of syntheticimages compared to real images, the desired performance cannot be achieved.Real images consist of multiple forms of light orientation, while syntheticimages consist of a uniform light orientation. These features are considered tobe characteristic of outdoor and indoor scenes, respectively. To solve thisproblem, the previous method learned a model to improve the realism of thesynthetic image. Different from the previous methods, this paper try to purifyreal image by extracting discriminative and robust features to convert outdoorreal images to indoor synthetic images. In this paper, we first introduce thesegmentation masks to construct RGB-mask pairs as inputs, then we design aattention-guided style transfer network to learn style features separately fromthe attention and bkgd(background) region , learn content features from fulland attention region. Moreover, we propose a novel region-level task-guidedloss to restrain the features learnt from style and content. Experiments wereperformed using mixed studies (qualitative and quantitative) methods todemonstrate the possibility of purifying real images in complex directions. Weevaluate the proposed method on three public datasets, including LPW, COCO andMPIIGaze. Extensive experimental results show that the proposed method iseffective and achieves the state-of-the-art results.

Purifying Real Images with an Attention-guided Style Transfer Network for Gaze Estimation