We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one. The proposed Xing generator consists of two generation branches that model the person's appearance and shape information, respectively. Moreover, we propose two novel blocks to effectively transfer and update the person's shape and appearance embeddings in a crossing way to mutually improve each other, which has not been considered by any other existing GAN-based image generation work. Extensive experiments on two challenging datasets, i.e., Market-1501 and DeepFashion, demonstrate that the proposed XingGAN advances the state-of-the-art performance both in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/XingGAN.

该研究提出了一种新颖的生成对抗网络 (XingGAN)，用于人物图像生成任务，即将给定人物的姿势转换为所需的姿势。该方法使用两个生成分支建模人物的外观和形状信息，并利用交叉的方式更新这些信息以相互提高。实验表明，该方法在两个具有挑战性的数据集上的性能比现有的图像生成方法均有所提高。

XingGAN的人物图像生成