Interactive deformation‐driven person silhouette image synthesis
Di Kong, Lili Wan, Zhizhuo Zhang, Wanru Xu, Shenghui Wang · Concurrency and Computation Practice and Experience · 2020
Abstract This paper addresses the problem of synthesizing person silhouette images. Previous methods on human‐centric image synthesis deal with normal images taken under consistent lighting conditions. However, a silhouette image has an inconsistent lighting appearance, in which the dark subject and the bright background form a strong contrast. Therefore, it brings great challenges to person silhouette image synthesis. We present a staged method to synthesize realistic person silhouette photos with various poses. The purpose of our method is to give ordinary users the opportunities to interactively adjust the pose of a person in a silhouette photo. The method consists of four main steps: person detection, person analysis, interactive deformation, and image synthesis. To obtain the shape of the target person, we propose a silhouette image segmentation algorithm combined with person detection. Moreover, we also present an effective image inpainting approach to complement a silhouette image with an irregular hole. Experimental results show that the proposed method can generate a set of realistic person silhouette images with interactively changed poses.