XingGAN for Person Image Generation

• URL: http://arxiv.org/abs/2007.09278v1
• Date: Fri, 17 Jul 2020 23:40:22 GMT
• Title: XingGAN for Person Image Generation
• Authors: Hao Tang, Song Bai, Li Zhang, Philip H.S. Torr, Nicu Sebe
• Abstract summary: We propose a novel Generative Adversarial Network (XingGAN) for person image generation tasks. XingGAN consists of two generation branches that model the person's appearance and shape information. We show that the proposed XingGAN advances the state-of-the-art performance in terms of objective quantitative scores and subjective visual realness.
• Score: 149.54517767056382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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