A Neural Temporal Model for Human Motion Prediction

Anand Gopalakrishnan, Ankur Mali, Dan Kifer, C. Lee Giles, Alexander G. Ororbia

Abstract:

We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-theart 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 multistep, 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