Human Pose Forecasting
8 papers with code • 3 benchmarks • 4 datasets
Human pose forecasting is the task of detecting and predicting future human poses.
( Image credit: EgoPose )
Human3.6M
3DPW
AMASS
PATS
Most implemented papers
Learning Trajectory Dependencies for Human Motion Prediction
wei-mao-2019/LearnTrajDep
•
•
ICCV 2019
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.
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
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.
Learning to Forecast and Refine Residual Motion for Image-to-Video Generation
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.
Action-Agnostic Human Pose Forecasting
eddyhkchiu/pose_forecast_wacv
•
•
In this paper, we propose a new action-agnostic method for short- and long-term human pose forecasting.
Ego-Pose Estimation and Forecasting as Real-Time PD Control
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.
DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
Khrylx/DLow
•
•
ECCV 2020
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.
Robust Motion In-betweening
To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3. 6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation.
Space-Time-Separable Graph Convolutional Network for Pose Forecasting
For the first time, STS-GCN models the human pose dynamics only with a graph convolutional network (GCN), including the temporal evolution and the spatial joint interaction within a single-graph framework, which allows the cross-talk of motion and spatial correlations.
