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On the Integration of Optical Flow and Action Recognition
Laura Sevilla-Lara, Yiyi Liao, Fatma Guney +3
Most of the top performing action recognition methods use optical flow as a "black box" input. Here we take a deeper look at the combination of flow and action recognition, and inv…
End-to-end Recovery of Human Shape and Pose
Angjoo Kanazawa, Michael J. Black, David W. Jacobs +1
We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that c…
Towards Accurate Markerless Human Shape and Pose Estimation over Time
Yinghao Huang, Federica Bogo, Christoph Lassner +4
Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios. Here we pr…
On human motion prediction using recurrent neural networks
Julieta Martinez, Michael J. Black, Javier Romero
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and moti…
Optical Flow in Mostly Rigid Scenes
Jonas Wulff, Laura Sevilla-Lara, Michael J. Black
The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering mo…
Detailed, accurate, human shape estimation from clothed 3D scan sequences
Chao Zhang, Sergi Pujades, Michael Black +1
We address the problem of estimating human pose and body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual tr…