Karl Pertsch

I am a first year PhD student in the Cognitive Learning for Vision and Robotics Lab (CLVR) at the University of Southern California where I work on deep learning, computer vision and robotics with Professor Joseph Lim.

Before joining CLVR I obtained my diploma in EE from TU Dresden, Germany woring with Professor Carsten Rother. I also got the chance to spend one year as a Fulbright Scholar in the GRASP Lab at the University of Pennsylvania working with Professor Kostas Daniilidis.

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  • [December 2018] I presented our work on unsupervised learning of agent's action spaces at the Infer2Control workshop at NeurIPS 2018 in Montreal.

  • [August 2018] I joined USC's PhD program in Computer Science, working at CLVR lab with Joseph Lim.

  • [June 2018] New ArXiv preprint on unsupervised discovery of agent's action spaces through stochastic video prediction.

  • [August 2017] Starting my one year Fulbright research stay in Kostas Daniilidis group at UPenn.


I'm interested in machine learning, computer vision and robotics. At the moment I am working on unsupervised learning of predictive models that can be used for planning and control. Before that I worked on more 'classic' computer vision. i.e. 6DoF object pose estimation.

Unsupervised Learning of Sensorimotor Affordances by Stochastic Future Prediction
Oleh Rybkin*, Karl Pertsch*, Andrew Jaegle, Kosta Derpanis, Kostas Daniilidis
ArXiv, 2018
project page / arXiv / poster

The method learns agent's action spaces from pure video data. This can be used e.g. to transplant a trajectory of actions from one video into another.

Hover over image (or tap the screen) to see the video.

iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
Omid Hosseini Jafari*, Siva Karthik Mustikovela*, Karl Pertsch, Eric Brachmann, Carsten Rother
Asian Conference on Computer Vision (ACCV), 2018

Combining a CNN-based regression of dense on-object surface labeling with RANSAC-based pose fitting for accurate 6DoF pose estimation of texture-less objects under heavy occlusion.

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