Hierarchical Style-based Networks for Motion Synthesis

Understand

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.