Pose guided human image synthesis by view disentanglement and enhanced weighting loss

Abstract

View synthesis aims at generating a novel, unseen view of an object. This is a challenging task in the presence of occlusions and asymmetries. In this paper, we present View-Disentangled Generator (VDG), a two-stage deep network for pose-guided human-image generation that performs coarse view prediction followed by a refinement stage. In the first stage, the network predicts the output from a target human pose, the source-image and the corresponding human pose, which are processed in different branches separately. This enables the network to learn a disentangled representation from the source and target view. In the second stage, the coarse output from the first stage is refined by adversarial training. Specifically, we introduce a masked version of the structural similarity loss that facilitates the network to focus on generating a higher quality view. Experiments on Market-1501 and DeepFashion demonstrate the effectiveness of the proposed generator.