Human Pose Forecasting
38 papers with code • 5 benchmarks • 5 datasets
Human pose forecasting is the task of detecting and predicting future human poses.
( Image credit: EgoPose )
Human3.6M
3DPW
AMASS
GTA-IM Dataset
PATS
Latest papers with no code
Exploring 3D Human Pose Estimation and Forecasting from the Robot's Perspective: The HARPER Dataset
The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users.
Personalized Pose Forecasting
Human pose forecasting is the task of predicting articulated human motion given past human motion.
Dual Quaternion Rotational and Translational Equivariance in 3D Rigid Motion Modelling
To overcome these limitations, we employ a dual quaternion representation of rigid motions in the 3D space that jointly describes rotations and translations of point sets, processing each of the points as a single entity.
AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations
Anytime 3D human pose forecasting is crucial to synchronous real-world human-machine interaction, where the term ``anytime" corresponds to predicting human pose at any real-valued time step.
Towards Accurate Human Motion Prediction via Iterative Refinement
To address the problem, in this work we propose FreqMRN, a human motion prediction framework that takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion.
Multi-Graph Convolution Network for Pose Forecasting
The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks.
Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory.
Imitation Learning for Human Pose Prediction
Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of various architectures of recurrent neural networks.
Diverse Trajectory Forecasting with Determinantal Point Processes
To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP).
Human Pose Forecasting via Deep Markov Models
Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving.
