6 papers with code • 1 benchmarks • 2 datasets
Human pose forecasting is the task of detecting and predicting future human poses.
( Image credit: EgoPose )
The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps.
Ranked #4 on Skeleton Based Action Recognition on CAD-120
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 #1 on Human Pose Forecasting on Human3.6M
We propose the use of a proportional-derivative (PD) control based policy learned via reinforcement learning (RL) to estimate and forecast 3D human pose from egocentric videos.
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.
We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object.