  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.
