  Human motion prediction from historical pose sequence is at the core of many
applications in machine intelligence. However, in current state-of-the-art
methods, the predicted future motion is confined within the same activity. One
can neither generate predictions that differ from the current activity, nor
manipulate the body parts to explore various future possibilities. Undoubtedly,
this greatly limits the usefulness and applicability of motion prediction. In
this paper, we propose a generalization of the human motion prediction task in
which control parameters can be readily incorporated to adjust the forecasted
motion. Our method is compelling in that it enables manipulable motion
prediction across activity types and allows customization of the human movement
in a variety of fine-grained ways. To this aim, a simple yet effective
composite GAN structure, consisting of local GANs for different body parts and
aggregated via a global GAN is presented. The local GANs game in lower
dimensions, while the global GAN adjusts in high dimensional space to avoid
mode collapse. Extensive experiments show that our method outperforms
state-of-the-art. The codes are available at
https://github.com/herolvkd/AM-GAN.
