  Recent works have shown Generative Adversarial Networks (GANs) to be
particularly effective in image-to-image translations. However, in tasks such
as body pose and hand gesture translation, existing methods usually require
precise annotations, e.g. key-points or skeletons, which are time-consuming to
draw. In this work, we propose a novel GAN architecture that decouples the
required annotations into a category label - that specifies the gesture type -
and a simple-to-draw category-independent conditional map - that expresses the
location, rotation and size of the hand gesture. Our architecture synthesizes
the target gesture while preserving the background context, thus effectively
dealing with gesture translation in the wild. To this aim, we use an attention
module and a rolling guidance approach, which loops the generated images back
into the network and produces higher quality images compared to competing
works. Thus, our GAN learns to generate new images from simple annotations
without requiring key-points or skeleton labels. Results on two public datasets
show that our method outperforms state of the art approaches both
quantitatively and qualitatively. To the best of our knowledge, no work so far
has addressed the gesture-to-gesture translation in the wild by requiring
user-friendly annotations.
