Multistage Adversarial Losses for Pose-Based Human Image Synthesis
Human image synthesis has extensive practical applications e.g. person re-identification and data augmentation for human pose estimation. However, it is much more challenging than rigid object synthesis, e.g. cars and chairs, due to the variability of human posture. In this paper, we propose a pose-based human image synthesis method which can keep the human posture unchanged in novel viewpoints. Furthermore, we adopt multistage adversarial losses separately for the foreground and background generation, which fully exploits the multi-modal characteristics of generative loss to generate more realistic looking images. We perform extensive experiments on the Human3.6M dataset and verify the effectiveness of each stage of our method. The generated human images not only keep the same pose as the input image, but also have clear detailed foreground and background. The quantitative comparison results illustrate that our approach achieves much better results than several state-of-the-art methods.
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