  We tackle the problem of generating long-term 3D human motion from multiple
action labels. Two main previous approaches, such as action- and
motion-conditioned methods, have limitations to solve this problem. The
action-conditioned methods generate a sequence of motion from a single action.
Hence, it cannot generate long-term motions composed of multiple actions and
transitions between actions. Meanwhile, the motion-conditioned methods generate
future motions from initial motion. The generated future motions only depend on
the past, so they are not controllable by the user's desired actions. We
present MultiAct, the first framework to generate long-term 3D human motion
from multiple action labels. MultiAct takes account of both action and motion
conditions with a unified recurrent generation system. It repetitively takes
the previous motion and action label; then, it generates a smooth transition
and the motion of the given action. As a result, MultiAct produces realistic
long-term motion controlled by the given sequence of multiple action labels.
Codes are available here at https://github.com/TaeryungLee/MultiAct_RELEASE.
