  The task of predicting human motion is complicated by the natural
heterogeneity and compositionality of actions, necessitating robustness to
distributional shifts as far as out-of-distribution (OoD). Here we formulate a
new OoD benchmark based on the Human3.6M and CMU motion capture datasets, and
introduce a hybrid framework for hardening discriminative architectures to OoD
failure by augmenting them with a generative model. When applied to current
state-of-the-art discriminative models, we show that the proposed approach
improves OoD robustness without sacrificing in-distribution performance, and
can theoretically facilitate model interpretability. We suggest human motion
predictors ought to be constructed with OoD challenges in mind, and provide an
extensible general framework for hardening diverse discriminative architectures
to extreme distributional shift. The code is available at
https://github.com/bouracha/OoDMotion.
