# Generative Model-Enhanced Human Motion Prediction

arXiv: https://arxiv.org/abs/2010.11699 · HTML: https://mlsanity.com/paper/2010.11699/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning](https://mlsanity.com/paper/2106.03609/) (2021)
- [Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion](https://mlsanity.com/paper/2111.12602/) (2021)
- [Improving Pose Estimation through Contextual Activity Fusion](https://mlsanity.com/paper/2111.02500/) (2021)

## Abstract (arXiv)

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.