  We address the computational problem of novel human pose synthesis. Given an
image of a person and a desired pose, we produce a depiction of that person in
that pose, retaining the appearance of both the person and background. We
present a modular generative neural network that synthesizes unseen poses using
training pairs of images and poses taken from human action videos. Our network
separates a scene into different body part and background layers, moves body
parts to new locations and refines their appearances, and composites the new
foreground with a hole-filled background. These subtasks, implemented with
separate modules, are trained jointly using only a single target image as a
supervised label. We use an adversarial discriminator to force our network to
synthesize realistic details conditioned on pose. We demonstrate image
synthesis results on three action classes: golf, yoga/workouts and tennis, and
show that our method produces accurate results within action classes as well as
across action classes. Given a sequence of desired poses, we also produce
coherent videos of actions.
