  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.
