Inspired by the strong ties between vision and language, the two intimate human sensing
and communication modalities, our paper aims to explore the generation of 3D human full …

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 …

Action recognition is a relatively established task, where given an input sequence of human
motion, the goal is to predict its action category. This paper, on the other hand, considers a …

Stochastic human motion prediction (HMP) has generally been tackled with generative
adversarial networks and variational autoencoders. Most prior works aim at predicting highly …

In this work we present a novel, robust transition generation technique that can serve as a
new tool for 3D animators, based on adversarial recurrent neural networks. The system …

Multi-frame human pose estimation in complicated situations is challenging. Although state-
of-the-art human joints detectors have demonstrated remarkable results for static images …

Abstract 3D human motion prediction, ie, forecasting future sequences from given historical
poses, is a fundamental task for action analysis, human-computer interaction, machine …

Human pose estimation aims at localizing human anatomical keypoints or body parts in the
input data (eg, images, videos, or signals). It forms a crucial component in enabling …