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 …

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 …

Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

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

Abstract 3D human motion prediction, predicting future poses from a given sequence, is an
issue of great significance and challenge in computer vision and machine intelligence …

Traditional handcrafted animation often heavily relies on creating keyframes while the in-
betweening is automatically generated through spline-based interpolation. Animators have …

Predicting human motion from a historical pose sequence is at the core of many applications
in computer vision. Current state-of-the-art methods concentrate on learning motion contexts …

As an important and challenging task in computer vision, human motion prediction aims to
predict the future human motion sequence from a given historical sequence. Though the …

Skeleton-based action recognition is widely used in varied areas, eg, surveillance and
human-machine interaction. Existing models are mainly learned in a supervised manner …