  In this paper, we address the problem of generating person images conditioned
on both pose and appearance information. Specifically, given an image xa of a
person and a target pose P(xb), extracted from a different image xb, we
synthesize a new image of that person in pose P(xb), while preserving the
visual details in xa. In order to deal with pixel-to-pixel misalignments caused
by the pose differences between P(xa) and P(xb), we introduce deformable skip
connections in the generator of our Generative Adversarial Network. Moreover, a
nearest-neighbour loss is proposed instead of the common L1 and L2 losses in
order to match the details of the generated image with the target image.
Quantitative and qualitative results, using common datasets and protocols
recently proposed for this task, show that our approach is competitive with
respect to the state of the art. Moreover, we conduct an extensive evaluation
using off-the-shell person re-identification (Re-ID) systems trained with
person-generation based augmented data, which is one of the main important
applications for this task. Our experiments show that our Deformable GANs can
significantly boost the Re-ID accuracy and are even better than
data-augmentation methods specifically trained using Re-ID losses.
