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max planck institut
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Mueller, Franziska

Franziska Mueller

Max-Planck-Institut für Informatik
Saarland Informatics Campus
Department 4: Computer Graphics
Campus E1 4, Room 211C
66123 Saarbrücken

Email: Get my email address via email
Phone: +49 681 9325 4057

I'm a 3rd year Ph.D. student in the Graphics, Vision and Video group supervised by Prof. Dr. Christian Theobalt.

Research Interests


GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB

F. Mueller F. Bernard O. Sotnychenko D. Mehta S. Sridhar D. Casas C. Theobalt

Spotlight @ Computer Vision and Pattern Recognition (CVPR) 2018

We propose a novel method for real-time hand tracking from monocular RGB input which combines a CNN-based regressor and a kinematic optimization framework. To enhance our synthetic training data, we introduce a new geometrically consistent image-to-image translator for unpaired examples.

[pdf], [supplemental], [video], [project page]

Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor

F. Mueller D. Mehta O. Sotnychenko S. Sridhar D. Casas C. Theobalt

International Conference on Computer Vision (ICCV) 2017

We present a method for real-time hand tracking under occlusion in cluttered egocentric scenes from a single RGB-D camera. To enable training of our machine learning components, we introduce a new large-scale dataset SynthHands which was captured using a mixed reality approach. Furthermore, we propose a real benchmark dataset EgoDexter which provides annotated fingertip positions.

[pdf], [supplemental], [video], [project page]

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

S. Sridhar F. Mueller M. Zollhöfer D. Casas A. Oulasvirta C. Theobalt

European Conference on Computer Vision (ECCV) 2016

In this work we tackle the especially challenging problem of tracking joint hand and object motion while running at realtime framerates.

[pdf], [supplemental], [poster], [video], [project page], [Dexter+Object dataset]

Fast and Robust Hand Tracking Using Detection-Guided Optimization

S. Sridhar F. Mueller A. Oulasvirta C. Theobalt

Proc. Computer Vision and Pattern Recognition (CVPR) 2015

We present a novel approach for real-time tracking of hand motion from a single commodity depth camera. The method combines a generative pose optimization framework with discriminative part label evidence for robustness and recovery.

[pdf], [supplemental], [poster], [video], [project page]

Awards & Honors