Deep representation learning for human motion prediction and classification

Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work Bütepage et al. propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. The method outperforms the recent state of the art in skeletal motion prediction.