Unsupervised and Generic Short-Term Anticipation of Human Body Motions
This addresses the need for interpretable motion anticipation in robotics or human-computer interaction, but it is incremental as it builds on existing decomposition techniques.
The paper tackled the problem of anticipating human body motions with interpretability, proposing Dynamic Mode Decomposition with delays to achieve comparable or better errors for very short times (<0.4 sec) compared to a recurrent neural network method.
Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable or even better for very short anticipation times ($<0.4$ sec) to a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of ``factors''. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence is of a generic nature.