Multiscale Residual Learning of Graph Convolutional Sequence Chunks for
  Human Motion Prediction
Mohsen Zand Ali Etemad Michael Greenspan
A new method is proposed for human motion prediction by learning temporal and
spatial dependencies. Recently, multiscale graphs have been developed to model
the human body at higher abstraction levels, resulting in more stable motion
prediction. Current methods however predetermine scale levels and combine
spatially proximal joints to generate coarser scales based on human priors,
even though movement patterns in different motion sequences vary and do not
fully comply with a fixed graph of spatially connected joints. Another problem
with graph convolutional methods is mode collapse, in which predicted poses
converge around a mean pose with no discernible movements, particularly in
long-term predictions. To tackle these issues, we propose ResChunk, an
end-to-end network which explores dynamically correlated body components based
on the pairwise relationships between all joints in individual sequences.
ResChunk is trained to learn the residuals between target sequence chunks in an
autoregressive manner to enforce the temporal connectivities between
consecutive chunks. It is hence a sequence-to-sequence prediction network which
considers dynamic spatio-temporal features of sequences at multiple levels. Our
experiments on two challenging benchmark datasets, CMU Mocap and Human3.6M,
demonstrate that our proposed method is able to effectively model the sequence
information for motion prediction and outperform other techniques to set a new
state-of-the-art. Our code is available at
https://github.com/MohsenZand/ResChunk.