Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences

@article{Taylor2016EfficientAP,
  title={Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences},
  author={Jonathan Taylor and Lucas Bordeaux and Thomas J. Cashman and Bob Corish and Cem Keskin and Toby Sharp and Eduardo Soto and David Sweeney and Julien P. C. Valentin and Benjamin Luff and Arran Topalian and Erroll Wood and Sameh Khamis and Pushmeet Kohli and Shahram Izadi and Richard Banks and Andrew W. Fitzgibbon and Jamie Shotton},
  journal={ACM Trans. Graph.},
  year={2016},
  volume={35},
  pages={143:1-143:12}
}
Fully articulated hand tracking promises to enable fundamentally new interactions with virtual and augmented worlds, but the limited accuracy and efficiency of current systems has prevented widespread adoption. Today's dominant paradigm uses machine learning for initialization and recovery followed by iterative model-fitting optimization to achieve a detailed pose fit. We follow this paradigm, but make several changes to the model-fitting, namely using: (1) a more discriminative objective… CONTINUE READING
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