Accurate, Robust, and Flexible Real-time Hand Tracking
We present a new real-time hand tracking system based on a single depth camera. The system can accurately reconstruct complex hand poses across a variety of subjects. It also allows for robust tracking, rapidly recovering from any temporary failures. Most uniquely, our tracker is highly flexible, dramatically improving upon previous approaches which have focused on front-facing close-range scenarios. This flexibility opens up new possibilities for human-computer interaction with examples including tracking at distances from tens of centimeters through to several meters (for controlling the TV at a distance), supporting tracking using a moving depth camera (for mobile scenarios), and arbitrary camera placements (for VR headsets). These features are achieved through a new pipeline that combines a multi-layered discriminative reinitialization strategy for per-frame pose estimation, followed by a generative model-fitting stage. We provide extensive technical details and a detailed qualitative and quantitative analysis.
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Alon Vinnikov
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Andrew Fitzgibbon
Partner Researcher
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Daniel Freedman
Principal Applied Researcher
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Eyal Krupka
Partner Research Manager
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Ido Leichter
Senior Researcher
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Jamie Shotton
Partner Director of Science
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Pushmeet Kohli
Principal Research Manager Director of Research Microsoft Research
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Toby Sharp
Principal Software Scientist Microsoft AI / HoloLens
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Yichen Wei
Senior Researcher
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