Recently human–robot collaboration (HRC) has emerged as a promising paradigm for mass
personalization in manufacturing owing to the potential to fully exploit the strength of human …

This paper reviews model-based and model-free approaches for continuous prediction of
human joint motion using surface electromyography (EMG) signals. The review focuses on …

This paper tackles the problem of human motion prediction, consisting in forecasting future
body poses from historically observed sequences. State-of-the-art approaches provide good …

We propose a novel Transformer-based architecture for the task of generative modelling of
3D human motion. Previous work commonly relies on RNN-based models considering …

Human motion prediction is a challenging and important task in many computer vision
application domains. Existing work only implicitly models the spatial structure of the human …

Abstract We propose a Generative Adversarial Network (GAN) to forecast 3D human motion
given a sequence of past 3D skeleton poses. While recent GANs have shown promising …

Pedestrian trajectory prediction is an essential task in robotic applications such as
autonomous driving and robot navigation. State-of-the-art trajectory predictors use a …

We propose a new architecture for the learning of predictive spatio-temporal motion models
from data alone. Our approach, dubbed the Dropout Autoencoder LSTM (DAELSTM), is …

Graph Convolutional Networks (GCN) which typically follows a neural message passing
framework to model dependencies among skeletal joints has achieved high success in …

Previous work on predicting or generating 3D human pose sequences regresses either joint
rotations or joint positions. The former strategy is prone to error accumulation along the …