# On human motion prediction using recurrent neural networks

## TL;DR

This paper critically examines the use of recurrent neural networks for human motion prediction, revealing that simple baselines can outperform complex models, and proposes improved RNN architectures for better performance.

## Contribution

The paper analyzes current RNN methods for human motion prediction and introduces three modifications that lead to a simple, scalable, and state-of-the-art RNN model.

## Key findings

- Simple baselines can outperform complex RNN models.
- Proposed modifications improve RNN performance.
- New architecture achieves state-of-the-art results.

## Abstract

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state-of-the-art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which result in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.