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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 still beachieved. Real images consist of multiple forms of light orientation, whilesynthetic images consist of a uniform light orientation. These features areconsidered to be characteristic of outdoor and indoor scenes, respectively. Tosolve this problem, the previous method learned a model to improve the realismof the synthetic image. Different from the previous methods, this paper takesthe first step to purify real images. Through the style transfer task, thedistribution of outdoor real images is converted into indoor synthetic images,thereby reducing the influence of light. Therefore, this paper proposes areal-time style transfer network that preserves image content information (eg,gaze direction, pupil center position) of an input image (real image) whileinferring style information (eg, image color structure, semantic features) ofstyle image (synthetic image). In addition, the network accelerates theconvergence speed of the model and adapts to multi-scale images. Experimentswere performed using mixed studies (qualitative and quantitative) methods todemonstrate the possibility of purifying real images in complex directions.Qualitatively, it compares the proposed method with the available methods in aseries of indoor and outdoor scenarios of the LPW dataset. In quantitativeterms, it evaluates the purified image by training a gaze estimation model onthe cross data set. The results show a significant improvement over thebaseline method compared to the raw real image.

Purifying Naturalistic Images through a Real-time Style Transfer Semantics Network