Human motion prediction
26 papers with code • 0 benchmarks • 2 datasets
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Ranked #1 on Trajectory Forecasting on ActEV
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene.
The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
Ranked #1 on Trajectory Forecasting on ForkingPaths
In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints.
Ranked #2 on Human Pose Forecasting on Human3.6M
We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution.
Ranked #1 on Trajectory Prediction on Stanford Drone (FDE (in world coordinates) metric)
The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning.
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment.
Ranked #1 on Trajectory Prediction on Stanford Drone (ADE (in world coordinates) metric)
To obtain samples from a pretrained generative model, most existing generative human motion prediction methods draw a set of independent Gaussian latent codes and convert them to motion samples.