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

  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},
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
Highly Influential
This paper has highly influenced 16 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 262 citations. REVIEW CITATIONS
156 Citations
24 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 156 extracted citations

263 Citations

Citations per Year
Semantic Scholar estimates that this publication has 263 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 24 references

Decision forests: A unified framework

  • A. Criminisi, J. Shotton, E. Konukoglu
  • NOW Publishers, To Appear
  • 2012
2 Excerpts

Similar Papers

Loading similar papers…