1 research outputs found
Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network
The primary goal of skeletal motion prediction is to generate future motion
by observing a sequence of 3D skeletons. A key challenge in motion prediction
is the fact that a motion can often be performed in several different ways,
with each consisting of its own configuration of poses and their
spatio-temporal dependencies, and as a result, the predicted poses often
converge to the motionless poses or non-human like motions in long-term
prediction. This leads us to define a hierarchical recurrent network model that
explicitly characterizes these internal configurations of poses and their local
and global spatio-temporal dependencies. The model introduces a latent vector
variable from the Lie algebra to represent spatial and temporal relations
simultaneously. Furthermore, a structured stack LSTM-based decoder is devised
to decode the predicted poses with a new loss function defined to estimate the
quantized weight of each body part in a pose. Empirical evaluations on
benchmark datasets suggest our approach significantly outperforms the
state-of-the-art methods on both short-term and long-term motion prediction.Comment: Accepted by the 24th European Conference on Artificial Intelligenc