  We propose novel neural temporal models for predicting and synthesizing human
motion, achieving state-of-the-art in modeling long-term motion trajectories
while being competitive with prior work in short-term prediction and requiring
significantly less computation. Key aspects of our proposed system include: 1)
a novel, two-level processing architecture that aids in generating planned
trajectories, 2) a simple set of easily computable features that integrate
derivative information, and 3) a novel multi-objective loss function that helps
the model to slowly progress from simple next-step prediction to the harder
task of multi-step, closed-loop prediction. Our results demonstrate that these
innovations improve the modeling of long-term motion trajectories. Finally, we
propose a novel metric, called Normalized Power Spectrum Similarity (NPSS), to
evaluate the long-term predictive ability of motion synthesis models,
complementing the popular mean-squared error (MSE) measure of Euler joint
angles over time. We conduct a user study to determine if the proposed NPSS
correlates with human evaluation of long-term motion more strongly than MSE and
find that it indeed does. We release code and additional results
(visualizations) for this paper at:
https://github.com/cr7anand/neural_temporal_models
