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Various neural network based methods are capable of anticipating human bodymotions from data for a short period of time. What these methods lack are theinterpretability and explainability of the network and its results. We proposeto use Dynamic Mode Decomposition with delays to represent and anticipate humanbody motions. Exploring the influence of the number of delays on thereconstruction and prediction of various motion classes, we show that theanticipation errors in our results are comparable or even better for very shortanticipation times ($<0.4$ sec) to a recurrent neural network based method. Weperceive our method as a first step towards the interpretability of the resultsby representing human body motions as linear combinations of ``factors''. Inaddition, compared to the neural network based methods large training times arenot needed. Actually, our methods do not even regress to any other motions thanthe one to be anticipated and hence is of a generic nature.

Unsupervised and Generic Short-Term Anticipation of Human Body Motions