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
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.
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.
BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence.
History Repeats Itself: Human Motion Prediction via Motion Attention
wei-mao-2019/HisRepItself
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ECCV 2020
Human motion prediction aims to forecast future human poses given a past motion.
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
google/next-prediction
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CVPR 2019
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
abduallahmohamed/Social-STGCNN
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CVPR 2020
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans.
Generative Model-Enhanced Human Motion Prediction
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD).
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.
Complex Gated Recurrent Neural Networks
v0lta/Complex-gated-recurrent-neural-networks
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NeurIPS 2018
Complex numbers have long been favoured for digital signal processing, yet complex representations rarely appear in deep learning architectures.
