10 papers · 1 filter
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
Andreas Blattmann, Timo Milbich, Michael Dorkenwald +1
How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident v…
Understanding Object Dynamics for Interactive Image-to-Video Synthesis
Andreas Blattmann, Timo Milbich, Michael Dorkenwald +1
What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level…
Stochastic Image-to-Video Synthesis using cINNs
Michael Dorkenwald, Timo Milbich, Andreas Blattmann +3
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 futur…
Behavior-Driven Synthesis of Human Dynamics
Andreas Blattmann, Timo Milbich, Michael Dorkenwald +1
Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of pos…
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich, Karsten Roth, Homanga Bharadhwaj +4
Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only gen…
PADS: Policy-Adapted Sampling for Visual Similarity Learning
Karsten Roth, Timo Milbich, Björn Ommer
Learning visual similarity requires to learn relations, typically between triplets of images. Albeit triplet approaches being powerful, their computational complexity mostly limits…