Search Results for author: Panna Felsen

Found 6 papers, 2 papers with code

Predicting 3D Human Dynamics from Video

1 code implementation ICCV 2019 Jason Y. Zhang, Panna Felsen, Angjoo Kanazawa, Jitendra Malik

In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input.

3D Human Dynamics 3D Human Pose Estimation +2

Learning 3D Human Dynamics from Video

1 code implementation CVPR 2019 Angjoo Kanazawa, Jason Y. Zhang, Panna Felsen, Jitendra Malik

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.

3D Human Dynamics

Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders

no code implementations ECCV 2018 Panna Felsen, Patrick Lucey, Sujoy Ganguly

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

no code implementations ICCV 2017 Panna Felsen, Pulkit Agrawal, Jitendra Malik

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

no code implementations ICCV 2015 Katerina Fragkiadaki, Sergey Levine, Panna Felsen, Jitendra Malik

We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture.

Human Dynamics Human Pose Forecasting +3

Learning to Segment Moving Objects in Videos

no code implementations CVPR 2015 Katerina Fragkiadaki, Pablo Arbelaez, Panna Felsen, Jitendra Malik

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.

Video Segmentation Video Semantic Segmentation

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