Jürgen Sturm

Group Leader RGB-D
Metaio

Deep Learning for Virtual Shopping

Metaio is the world leading augmented reality provider. We enable to virtually try-on glasses, earrings, and even new hair colors, which creates a completely new and immersive shopping experience: Customers can directly check out how a new product would look on them without the need of the real physical product. In my talk, I will give an overview of recent deep learning techniques developed at Metaio for face detection and face tracking. We recorded large datasets for face tracking and alignment, both for normal cameras and depth cameras such as Kinect. We use both convolutional networks and random forests for classification and shape regression. Special care was given to memory and compute optimization, so that our software runs in real-time on mobile devices such as smartphones and tablets. During my talk, I will give several live demos of our technology and how we make use of them to create a value chain together with our customers.

Dr. Jürgen Sturm heads the machine learning efforts at Metaio GmbH, the world-leading Augmented Reality technology provider. He and his team research deep learning techniques such as random forests to track and augment the human body on camera images. The goal of Metaio’s machine learning efforts is to create immersive virtual shopping experiences, for example, to try on sunglasses or earrings. Before he joined Metaio, he was a postdoctoral researcher in the Computer Vision group of Prof. Daniel Cremers at the Technical University of Munich, where he developed several novel methods for real-time camera tracking and 3D person scanning. In 2011, he obtained his PhD from the Autonomous Intelligent Systems lab headed by Prof. Wolfram Burgard at the University of Freiburg. He won several awards for his scientific work including the best dissertation award of the European Coordinating Committee of Artificial Intelligence (ECCAI) in 2011 and the TUM TeachInf best lecture award 2012 and 2013 for his course "Visual Navigation for Flying Robots".

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