Photo2Relief: Let Human in the Photograph Stand Out
Zhongping Ji Feifei Che Hanshuo Liu Ziyi Zhao Yu-Wei Zhang Wenping Wang
In this paper, we propose a technique for making humans in photographs
protrude like reliefs. Unlike previous methods which mostly focus on the face
and head, our method aims to generate art works that describe the whole body
activity of the character. One challenge is that there is no ground-truth for
supervised deep learning. We introduce a sigmoid variant function to manipulate
gradients tactfully and train our neural networks by equipping with a loss
function defined in gradient domain. The second challenge is that actual
photographs often across different light conditions. We used image-based
rendering technique to address this challenge and acquire rendering images and
depth data under different lighting conditions. To make a clear division of
labor in network modules, a two-scale architecture is proposed to create
high-quality relief from a single photograph. Extensive experimental results on
a variety of scenes show that our method is a highly effective solution for
generating digital 2.5D artwork from photographs.