Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

In recent years, machine learning (ML) techniques have been extensively investigated to
strengthen the understanding of the complex process dynamics underlying metal additive …

Human motion prediction is a challenging task due to the stochasticity and aperiodicity of
future poses. Recently, graph convolutional network has been proven to be very effective to …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

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 …

This paper presents a high-quality human motion prediction method that accurately predicts
future human poses given observed ones. Our method is based on the observation that a …

We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D
skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to …

Modeling real-world multidimensional time series can be particularly challenging when
these are sporadically observed (ie, sampling is irregular both in time and across …

We present a framework to synthesize character movements based on high level
parameters, such that the produced movements respect the manifold of human motion …

Abstract We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and
prediction of human body pose in videos and motion capture. The ERD model is a recurrent …