The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation

@article{Taylor2012TheVM,
  title={The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation},
  author={Jonathan Taylor and Jamie Shotton and Toby Sharp and Andrew W. Fitzgibbon},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2012},
  pages={103-110}
}
Fitting an articulated model to image data is often approached as an optimization over both model pose and model-to-image correspondence. For complex models such as humans, previous work has required a good initialization, or an alternating minimization between correspondence and pose. In this paper we investigate one-shot pose estimation: can we directly infer correspondences using a regression function trained to be invariant to body size and shape, and then optimize the model pose just once… CONTINUE READING
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Decision forests: A unified framework

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