Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

by   Mohsen Zand, et al.

A new method is proposed for human motion prediction by learning temporal and spatial dependencies. Recently, multiscale graphs have been developed to model the human body at higher abstraction levels, resulting in more stable motion prediction. Current methods however predetermine scale levels and combine spatially proximal joints to generate coarser scales based on human priors, even though movement patterns in different motion sequences vary and do not fully comply with a fixed graph of spatially connected joints. Another problem with graph convolutional methods is mode collapse, in which predicted poses converge around a mean pose with no discernible movements, particularly in long-term predictions. To tackle these issues, we propose ResChunk, an end-to-end network which explores dynamically correlated body components based on the pairwise relationships between all joints in individual sequences. ResChunk is trained to learn the residuals between target sequence chunks in an autoregressive manner to enforce the temporal connectivities between consecutive chunks. It is hence a sequence-to-sequence prediction network which considers dynamic spatio-temporal features of sequences at multiple levels. Our experiments on two challenging benchmark datasets, CMU Mocap and Human3.6M, demonstrate that our proposed method is able to effectively model the sequence information for motion prediction and outperform other techniques to set a new state-of-the-art. Our code is available at


Flow-based Autoregressive Structured Prediction of Human Motion

A new method is proposed for human motion predition by learning temporal...

MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction

Human motion prediction is a challenging task due to the stochasticity a...

Convolutional Autoencoders for Human Motion Infilling

In this paper we propose a convolutional autoencoder to address the prob...

Multi-level Motion Attention for Human Motion Prediction

Human motion prediction aims to forecast future human poses given a hist...

Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction

We propose novel dynamic multiscale graph neural networks (DMGNN) to pre...

Back to MLP: A Simple Baseline for Human Motion Prediction

This paper tackles the problem of human motion prediction, consisting in...

Aggregated Multi-GANs for Controlled 3D Human Motion Prediction

Human motion prediction from historical pose sequence is at the core of ...

Please sign up or login with your details

Forgot password? Click here to reset