Search Results for author: Michael Dorkenwald

Found 7 papers, 4 papers with code

iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

2 code implementations ICCV 2021 Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer

There will be distinctive movement, despite evident variations caused by the stochastic nature of our world.

Understanding Object Dynamics for Interactive Image-to-Video Synthesis

1 code implementation CVPR 2021 Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer

Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time.

Video Prediction

Stochastic Image-to-Video Synthesis using cINNs

1 code implementation CVPR 2021 Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G. Derpanis, Björn Ommer

Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame.

Video Understanding

Behavior-Driven Synthesis of Human Dynamics

1 code implementation CVPR 2021 Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer

Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence.

Human Dynamics

Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning

no code implementations16 Dec 2020 Biagio Brattoli, Uta Buechler, Michael Dorkenwald, Philipp Reiser, Linard Filli, Fritjof Helmchen, Anna-Sophia Wahl, Bjoern Ommer

A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement.

Unsupervised Magnification of Posture Deviations Across Subjects

no code implementations CVPR 2020 Michael Dorkenwald, Uta Buchler, Bjorn Ommer

We present an approach to unsupervised magnification of posture differences across individuals despite large deviations in appearance.

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