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.

本文提出了一种自我监督的方法来生成多样且自然的人体动作，通过分解生成任务并使用记忆库检索动作引用作为短程片段生成的源材料，并通过参数化的双向插值方案保证了生成运动的物理合理性和视觉自然性。在大规模的骨架数据集上，我们展示了这个方法在生成长距离、多样化和合理化运动方面的能力，并且这种方法能够适应未见数据，在动态世界中这种生成的序列有实实在在的效果。