Generating a photorealistic image with intended human pose is a promising yet challenging research topic for many applications such as smart photo editing, movie making, virtual try-on, and fashion display. In this paper, we present a novel deep generative model to transfer an image of a person from a given pose to a new pose while keeping fashion item consistent. In order to formulate the framework, we employ one generator and two discriminators for image synthesis. The generator includes an image encoder, a pose encoder and a decoder. The two encoders provide good representation of visual and geometrical context which will be utilized by the decoder in order to generate a photorealistic image. Unlike existing pose-guided image generation models, we exploit two discriminators to guide the synthesis process where one discriminator differentiates between generated image and real images (training samples), and another discriminator verifies the consistency of appearance between a target pose and a generated image. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth images. The proposed generative model is capable of synthesizing a photorealistic image of a person given a target pose. We have demonstrated our results by conducting rigorous experiments on two data sets, both quantitatively and qualitatively.

本文提出了一种基于新颖的深度生成模型的图像迁移方法，可以在保持服装一致的情况下将一个人的图像从一个给定的姿势转移至一个新的姿势，使用图像编码器、姿势编码器和解码器的结构，同时利用两个鉴别器来指导产生过程。经过严格的实验，在两个数据集上定量和定性地验证了该方法的效果。

使用深度生成模型进行姿势引导的时尚图像合成