CPD-GAN: Cascaded Pyramid Deformation GAN for Pose Transfer

Yuan Huang, Yuting Tang, Xiu Zheng, Jie Tang

Abstract: Pose-guided person image generation aims to synthesize person images in arbitrary poses. This task requires to perform spatial deformation on source images. Existing work often failed to transfer complex textures to generated images well. To solve this problem, we propose a novel network for this task. The network is called Cascaded Pyramid Deformation GAN(CPD-GAN), which can achieve more realistic results and conform more to the target person. In the extraction sub-network, the multi-scale feature modulation(MFM) blocks are proposed. A MFM block can fuse features at different scales into single scale feature. And in the genration sub-network, the dynamic transferring fusion(DTF) blocks are proposed to perform dynamic deformation on features from extraction sub-network and a cascaded pyramids structure is adopted to improve the quality of resulted complex textures. Experiments prove that our method performs better on pose transfer than other methods and utilizes each part of the network effectively.