Unified Privileged Knowledge Distillation Framework for Human Motion Prediction

Previous works on human motion prediction follow the pattern of building an extrapolation mapping between the sequence observed and the one to be predicted. However, the inherent difficulty of time-series extrapolation and complexity of human motion data still result in many failure cases. In this paper, we explore a longer horizon of sequence with more poses following behind, which breaks the limit in extrapolation problems that data/information on the other side of the predictive target is completely unknown. As these poses are unavailable for testing, we regard them as a privileged sequence, and propose a Two-stage Privileged Knowledge Distillation framework that incorporates privileged information in the forecasting process while avoiding direct use of it. Specifically, in the first stage, both the observed and privileged sequence are encoded for interpolation, with Privileged-sequence-Encoder (Priv-Encoder) learning privileged knowledge (PK) simultaneously. Then, in the second stage where privileged sequence is not observable, a novel PK-Simulator distills PK by approximating the behavior of Priv-Encoder, but only taking as input the observed sequence, to enable a PK-aware prediction pattern. Moreover, we present a One-stage version of this framework, using Shared Encoder that integrates the observation encoding in both interpolation and prediction branches to realize parallel training, which helps produce the most conducive PK to prediction pipeline. Experimental results show that our frameworks are model-agnostic, and can be applied to existing motion prediction models with encoder-decoder architecture to achieve improved performance.