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
Most implemented papers
On human motion prediction using recurrent neural networks
una-dinosauria/human-motion-prediction
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CVPR 2017
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.
Learning Trajectory Dependencies for Human Motion Prediction
wei-mao-2019/LearnTrajDep
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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.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat-D/GMVAE
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We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.
HP-GAN: Probabilistic 3D human motion prediction via GAN
ebarsoum/hpgan
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Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses.
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.
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space
In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model.
Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning
Our model effectively handles the multi-modality of human motion and the complexity of long-term multi-agent interactions, improving performance in complex environments.
The Pose Knows: Video Forecasting by Generating Pose Futures
First we explicitly model the high level structure of active objects in the scene---humans---and use a VAE to model the possible future movements of humans in the pose space.
Convolutional Sequence to Sequence Model for Human Dynamics
chaneyddtt/Convolutional-Sequence-to-Sequence-Model-for-Human-Dynamics
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CVPR 2018
Human motion modeling is a classic problem in computer vision and graphics.
Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective
apratimbhattacharyya18/CGM_BestOfMany
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For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.
