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In this paper, we propose a technique for making humans in photographsprotrude like reliefs. Unlike previous methods which mostly focus on the faceand head, our method aims to generate art works that describe the whole bodyactivity of the character. One challenge is that there is no ground-truth forsupervised deep learning. We introduce a sigmoid variant function to manipulategradients tactfully and train our neural networks by equipping with a lossfunction defined in gradient domain. The second challenge is that actualphotographs often across different light conditions. We used image-basedrendering technique to address this challenge and acquire rendering images anddepth data under different lighting conditions. To make a clear division oflabor in network modules, a two-scale architecture is proposed to createhigh-quality relief from a single photograph. Extensive experimental results ona variety of scenes show that our method is a highly effective solution forgenerating digital 2.5D artwork from photographs.

Photo2Relief: Let Human in the Photograph Stand Out