  Generating diverse and natural human motion is one of the long-standing goals
for creating intelligent characters in the animated world. In this paper, we
propose a self-supervised method for generating long-range, diverse and
plausible behaviors to achieve a specific goal location. Our proposed method
learns to model the motion of human by decomposing a long-range generation task
in a hierarchical manner. Given the starting and ending states, a memory bank
is used to retrieve motion references as source material for short-range clip
generation. We first propose to explicitly disentangle the provided motion
material into style and content counterparts via bi-linear transformation
modelling, where diverse synthesis is achieved by free-form combination of
these two components. The short-range clips are then connected to form a
long-range motion sequence. Without ground truth annotation, we propose a
parameterized bi-directional interpolation scheme to guarantee the physical
validity and visual naturalness of generated results. On large-scale skeleton
dataset, we show that the proposed method is able to synthesise long-range,
diverse and plausible motion, which is also generalizable to unseen motion data
during testing. Moreover, we demonstrate the generated sequences are useful as
subgoals for actual physical execution in the animated world.
