Attribute-Aware Pedestrian Image Editing.

Pedestrian image generation is a very challenging task. Existing generation methods have drawbacks including body distortion, inadequate visual details and large vague areas. In this paper, we propose Attribute-aware Pedestrian Image Editing (APIE) to address these problems based on given visual attributes. Our model denominated as APIE-Net, has three mechanisms including an attribute-aware segmentation network, a multi-scale discriminator and a latent-variable discriminator. Experiments on Market-1501 and DukeMTMC-reID datasets show that APIE-Net can generate satisfying pedestrian images with given attributes. Moreover, the generated images can augment the original datasets thus improve the performance in pedestrian-related tasks such as person re-identification (re-ID) and attribute prediction. Especially in person re-ID tasks our method outperforms state-of-the-art methods by a large margin.