Appearance and Pose-Conditioned Human Image Generation using Deformable GANs

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 an 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 themain 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.