  We propose a Generative Adversarial Network (GAN) to forecast 3D human motion
given a sequence of past 3D skeleton poses. While recent GANs have shown
promising results, they can only forecast plausible motion over relatively
short periods of time (few hundred milliseconds) and typically ignore the
absolute position of the skeleton w.r.t. the camera. Our scheme provides long
term predictions (two seconds or more) for both the body pose and its absolute
position. Our approach builds upon three main contributions. First, we
represent the data using a spatio-temporal tensor of 3D skeleton coordinates
which allows formulating the prediction problem as an inpainting one, for which
GANs work particularly well. Secondly, we design an architecture to learn the
joint distribution of body poses and global motion, capable to hypothesize
large chunks of the input 3D tensor with missing data. And finally, we argue
that the L2 metric, considered so far by most approaches, fails to capture the
actual distribution of long-term human motion. We propose two alternative
metrics, based on the distribution of frequencies, that are able to capture
more realistic motion patterns. Extensive experiments demonstrate our approach
to significantly improve the state of the art, while also handling situations
in which past observations are corrupted by occlusions, noise and missing
frames.
