  Human Pose Estimation is a low-level task useful forsurveillance, human
action recognition, and scene understandingat large. It also offers promising
perspectives for the animationof synthetic characters. For all these
applications, and especiallythe latter, estimating the positions of many joints
is desirablefor improved performance and realism. To this purpose, wepropose a
novel method called JUMPS for increasing the numberof joints in 2D pose
estimates and recovering occluded ormissing joints. We believe this is the
first attempt to addressthe issue. We build on a deep generative model that
combines aGenerative Adversarial Network (GAN) and an encoder. TheGAN learns
the distribution of high-resolution human posesequences, the encoder maps the
input low-resolution sequencesto its latent space. Inpainting is obtained by
computing the latentrepresentation whose decoding by the GAN generator
optimallymatches the joints locations at the input. Post-processing a 2Dpose
sequence using our method provides a richer representationof the character
motion. We show experimentally that thelocalization accuracy of the additional
joints is on average onpar with the original pose estimates.
