Human motion prediction from its historical poses is an essential task in computer vision; it is
successfully applied for human-machine interaction and intelligent driving. Recently …

Recurrent neural network (RNN) has become popular for human motion prediction thanks to
its ability to capture temporal dependencies. However, it has limited capacity in modeling the …

Human motion prediction is an increasingly interesting topic in computer vision and robotics.
In this paper, we propose a new end-to-end feedforward network, TrajectoryCNN, to predict …

Human motion prediction, which aims at predicting future human skeletons given the past
ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been …

Human motion prediction from motion capture data is a classical problem in the computer
vision, and conventional methods take the holistic human body as input. These methods …

Human motion prediction, ie, forecasting future body poses given observed pose sequence,
has typically been tackled with recurrent neural networks (RNNs). However, as evidenced …

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 …

Predicting future motion based on historical motion sequence is a fundamental problem in
computer vision, and it has wide applications in autonomous driving and robotics. Some …

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 …

In this paper, we tackle the task of scene-aware 3D human motion forecasting, which
consists of predicting future human poses given a 3D scene and a past human motion. A key …