  As a special case of common object removal, image person removal is playing
an increasingly important role in social media and criminal investigation
domains. Due to the integrity of person area and the complexity of human
posture, person removal has its own dilemmas. In this paper, we propose a novel
idea to tackle these problems from the perspective of data synthesis.
Concerning the lack of dedicated dataset for image person removal, two dataset
production methods are proposed to automatically generate images, masks and
ground truths respectively. Then, a learning framework similar to local image
degradation is proposed so that the masks can be used to guide the feature
extraction process and more texture information can be gathered for final
prediction. A coarse-to-fine training strategy is further applied to refine the
details. The data synthesis and learning framework combine well with each
other. Experimental results verify the effectiveness of our method
quantitatively and qualitatively, and the trained network proves to have good
generalization ability either on real or synthetic images.
