Human motion modelling is a classical problem at the intersection of graphics and computer
vision, with applications spanning human-computer interaction, motion synthesis, and …

We propose novel neural temporal models for predicting and synthesizing human motion,
achieving state-of-the-art in modeling long-term motion trajectories while being competitive …

Human motion modeling is a classic problem in com-puter vision and graphics. Challenges
in modeling human motion include high dimensional prediction as well as extremely …

This paper introduces a new generative deep learning network for human motion synthesis
and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial …

We propose to leverage Transformer architectures for non-autoregressive human motion
prediction. Our approach decodes elements in parallel from a query sequence, instead of …

Predicting and understanding human motion dynamics has many applications, such as
motion synthesis, augmented reality, security, and autonomous vehicles. Due to the recent …

Generative models of 3D human motion are often restricted to a small number of activities
and can therefore not generalize well to novel movements or applications. In this work we …

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 is a challenging problem due to the complicated human body
constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN …

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