  Data-driven modeling of human motions is ubiquitous in computer graphics and
computer vision applications, such as synthesizing realistic motions or
recognizing actions. Recent research has shown that such problems can be
approached by learning a natural motion manifold using deep learning to address
the shortcomings of traditional data-driven approaches. However, previous
methods can be sub-optimal for two reasons. First, the skeletal information has
not been fully utilized for feature extraction. Unlike images, it is difficult
to define spatial proximity in skeletal motions in the way that deep networks
can be applied. Second, motion is time-series data with strong multi-modal
temporal correlations. A frame could be followed by several candidate frames
leading to different motions; long-range dependencies exist where a number of
frames in the beginning correlate to a number of frames later. Ineffective
modeling would either under-estimate the multi-modality and variance, resulting
in featureless mean motion or over-estimate them resulting in jittery motions.
In this paper, we propose a new deep network to tackle these challenges by
creating a natural motion manifold that is versatile for many applications. The
network has a new spatial component for feature extraction. It is also equipped
with a new batch prediction model that predicts a large number of frames at
once, such that long-term temporally-based objective functions can be employed
to correctly learn the motion multi-modality and variances. With our system,
long-duration motions can be predicted/synthesized using an open-loop setup
where the motion retains the dynamics accurately. It can also be used for
denoising corrupted motions and synthesizing new motions with given control
signals. We demonstrate that our system can create superior results comparing
to existing work in multiple applications.
