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Found 6 papers, 2 papers with code
Predicting 3D Human Dynamics from Video
In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input.
Learning 3D Human Dynamics from Video
We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features.
Ranked #15 on
3D Human Pose Estimation
on 3DPW
(Acceleration Error metric)
Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders
Simultaneously and accurately forecasting the behavior of many interacting agents is imperative for computer vision applications to be widely deployed (e. g., autonomous vehicles, security, surveillance, sports).
What Will Happen Next? Forecasting Player Moves in Sports Videos
A large number of very popular team sports involve the act of one team trying to score a goal against the other.
Recurrent Network Models for Human Dynamics
We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture.
Ranked #8 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
Learning to Segment Moving Objects in Videos
We segment moving objects in videos by ranking spatio-temporal segment proposals according to "moving objectness": how likely they are to contain a moving object.
