Human motion prediction
59 papers with code • 0 benchmarks • 4 datasets
Action prediction is a pre-fact video understanding task, which focuses on future states, in other words, it needs to reason about future states or infer action labels before the end of action execution.
Benchmarks
These leaderboards are used to track progress in Human motion prediction
EMDB
GTA-IM Dataset
VIENA2
Most implemented papers
A Spatio-temporal Transformer for 3D Human Motion Prediction
eth-ait/motion-transformer
•
•
We propose a novel Transformer-based architecture for the task of generative modelling of 3D human motion.
DeepSSM: Deep State-Space Model for 3D Human Motion Prediction
lily2lab/deepssm
•
•
In contrast to prior works, we improve the multi-order modeling ability of human motion systems for more accurate predictions by building a deep state-space model (DeepSSM).
DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
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.
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction
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.
Long-term Human Motion Prediction with Scene Context
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene.
Motion Prediction Using Temporal Inception Module
tileb1/motion-prediction-tim
•
•
We argue that the diverse temporal scales are important as they allow us to look at the past frames with different receptive fields, which can lead to better predictions.
Long Term Motion Prediction Using Keyposes
Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving.
Multi-Person Extreme Motion Prediction
GUO-W/MultiMotion
•
•
CVPR 2022
In this paper, we explore this problem when dealing with humans performing collaborative tasks, we seek to predict the future motion of two interacted persons given two sequences of their past skeletons.
Multi-level Motion Attention for Human Motion Prediction
Whether based on recurrent or feed-forward neural networks, existing learning based methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities.
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses.
