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
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.
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics
xcyan/eccv18_mtvae
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ECCV 2018
Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode.
Action-Agnostic Human Pose Forecasting
eddyhkchiu/pose_forecast_wacv
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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
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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.
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.
Generating Smooth Pose Sequences for Diverse Human Motion Prediction
wei-mao-2019/gsps
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ICCV 2021
Recent progress in stochastic motion prediction, i. e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts.
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.
Intention-based Long-Term Human Motion Anticipation
zaverichintan/Intention-based-Long-Term-Human-Motion-Anticipation
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3DV 2021
Recently, a few works have been proposed to model the uncertainty of the future human motion.
