The ability to synthesize long-term human motion sequences in real-world
scenes can facilitate numerous applications. Previous approaches for
scene-aware motion synthesis are constrained by pre-defined target objects or
positions and thus limit the diversity of human-scene interactions for
synthesized motions. In this paper, we focus on the problem of synthesizing
diverse scene-aware human motions under the guidance of target action
sequences. To achieve this, we first decompose the diversity of scene-aware
human motions into three aspects, namely interaction diversity (e.g. sitting on
different objects with different poses in the given scenes), path diversity
(e.g. moving to the target locations following different paths), and the motion
diversity (e.g. having various body movements during moving). Based on this
factorized scheme, a hierarchical framework is proposed, with each sub-module
responsible for modeling one aspect. We assess the effectiveness of our
framework on two challenging datasets for scene-aware human motion synthesis.
The experiment results show that the proposed framework remarkably outperforms
previous methods in terms of diversity and naturalness.

本文针对现有智能场景下的动作合成技术存在的目标、位置预定及动作多样性不足等问题，提出了一种基于多样性因素的分层框架方法，以提高人类动作合成的自然度与多样性。实验表明，该框架在场景感知的人类动作合成中具有很好的效果。