Pose-driven attention-guided image generation for person re-Identification

•We propose a novel identity-preserving pose transfer network for person re-identification.•We propose a method to sequentially transfer an image to a target pose by a series of pose-guided attention modules.•The proposed approach preserves the identity of the person in the pose translated images by using the semantic-consistency loss.•An appearance discriminator is proposed to ensure that the fine image details are realistic after pose translation while a pose discriminator is introduced to ensure the target image correctly captures the shape defined by the target pose. Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system. One of the major challenges in person re-ID is pose variations across the camera network, which significantly affects the appearance of a person. Existing development data lack adequate pose variations to carry out effective training of person re-ID systems. To solve this issue, in this paper we propose an end-to-end pose-driven attention-guided generative adversarial network, to generate multiple poses of a person. We propose to attentively learn and transfer the subject pose through an attention mechanism. A semantic-consistency loss is proposed to preserve the semantic information of the person during pose transfer. To ensure fine image details are realistic after pose translation, an appearance discriminator is used while a pose discriminator is used to ensure the pose of the transferred images will exactly be the same as the target pose. We show that by incorporating the proposed approach in a person re-identification framework, realistic pose transferred images and state-of-the-art re-identification results can be achieved.